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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2307.03109 | 139 | 29 Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023. Uncovering ChatGPTâs Capabilities in Recommender Systems. arXiv preprint arXiv:2305.02182 (2023).
2023. Uncovering ChatGPTâs Capabilities in Recommender Systems. arXiv preprint arXiv:2305.02182 (2023). [30] Wei Dai, Jionghao Lin, Flora Jin, Tongguang Li, Yi-Shan Tsai, Dragan Gasevic, and Guanliang Chen. 2023. Can large
language models provide feedback to students? a case study on chatgpt. (2023).
[31] Xuan-Quy Dao and Ngoc-Bich Le. 2023. Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination. arXiv preprint arXiv:2306.06331 (2023).
[32] Joost CF de Winter. 2023. Can ChatGPT pass high school exams on English language comprehension. Researchgate. Preprint (2023). | 2307.03109#139 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 140 | [32] Joost CF de Winter. 2023. Can ChatGPT pass high school exams on English language comprehension. Researchgate. Preprint (2023).
[33] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248â255.
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[35] Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. 2023. Toxicity in chatgpt: Analyzing persona-assigned language models. arXiv preprint arXiv:2304.05335 (2023).
[36] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). | 2307.03109#140 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[56] Gaël Gendron, Qiming Bao, Michael Witbrock, and Gillian Dobbie. 2023. Large Language Models Are Not Abstract Reasoners. arXiv preprint arXiv:2305.19555 (2023). | 2307.03109#145 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 147 | [61] Taicheng Guo, Kehan Guo, Zhengwen Liang, Zhichun Guo, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang, et al. 2023. What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks. arXiv preprint arXiv:2305.18365 (2023).
[62] Thilo Hagendorff and Sarah Fabi. 2023. Human-Like Intuitive Behavior and Reasoning Biases Emerged in Language Models â and Disappeared in GPT-4. arXiv:2306.07622 [cs.CL]
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[65] Jochen Hartmann, Jasper Schwenzow, and Maximilian Witte. 2023. The political ideology of conversational AI: Converging evidence on ChatGPTâs pro-environmental, left-libertarian orientation. arXiv preprint arXiv:2301.01768 (2023). | 2307.03109#147 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 148 | [66] Qianyu He, Jie Zeng, Wenhao Huang, Lina Chen, Jin Xiao, Qianxi He, Xunzhe Zhou, Lida Chen, Xintao Wang, Yuncheng Huang, et al. 2023. Can Large Language Models Understand Real-World Complex Instructions? arXiv preprint arXiv:2309.09150 (2023).
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 149 | [69] Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. 2020. Aligning ai with shared human values. arXiv preprint arXiv:2008.02275 (2020).
[70] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 (2020).
[71] Dan Hendrycks, Collin Burns, Anya Chen, and Spencer Ball. 2021. Cuad: An expert-annotated nlp dataset for legal contract review. arXiv preprint arXiv:2103.06268 (2021).
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
# A Survey on Evaluation of Large Language Models
[72] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874 (2021). | 2307.03109#149 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 150 | [73] Jason Holmes, Zhengliang Liu, Lian Zhang, Yuzhen Ding, Terence T Sio, Lisa A McGee, Jonathan B Ashman, Xiang Li, Tianming Liu, Jiajian Shen, et al. 2023. Evaluating large language models on a highly-specialized topic, radiation oncology physics. arXiv preprint arXiv:2304.01938 (2023).
[74] Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022. TRUE: Re-evaluating factual consistency evaluation. arXiv preprint arXiv:2204.04991 (2022).
[75] Zhaoyi Joey Hou, Li Zhang, and Chris Callison-Burch. 2023. Choice-75: A Dataset on Decision Branching in Script Learning. arXiv preprint arXiv:2309.11737 (2023). | 2307.03109#150 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 151 | [76] Jen-tse Huang, Man Ho Lam, Eric John Li, Shujie Ren, Wenxuan Wang, Wenxiang Jiao, Zhaopeng Tu, and Michael R. Lyu. 2023. Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench. arXiv preprint arXiv:2308.03656 (2023).
[77] Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Qiang Liu, et al. 2023. Language is not all you need: Aligning perception with language models. arXiv preprint arXiv:2302.14045 (2023).
[78] Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, et al. 2023. C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models. arXiv preprint arXiv:2305.08322 (2023). | 2307.03109#151 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 152 | [79] Yue Huang, Qihui Zhang, Philip S. Y, and Lichao Sun. 2023. TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models. arXiv:2306.11507 [cs.CL]
[80] HuggingFace. 2023. Open-source Large Language Models Leaderboard. https://huggingface.co/spaces/ HuggingFaceH4/open_llm_leaderboard.
[81] Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, and Jimmy Huang. 2023. Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers. arXiv preprint arXiv:2306.04504 (2023). [82] Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023. Bring Your Own Data! Self-Supervised Evaluation for Large Language Models. arXiv preprint arXiv:2306.13651 (2023). | 2307.03109#152 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 153 | [83] Malin Jansson, Stefan Hrastinski, Stefan Stenbom, and Fredrik Enoksson. 2021. Online question and answer sessions: How students support their own and other studentsâ processes of inquiry in a text-based learning environment. The Internet and Higher Education 51 (2021), 100817.
[84] Sophie Jentzsch and Kristian Kersting. 2023. ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models. arXiv preprint arXiv:2306.04563 (2023).
[85] Jiaming Ji, Mickel Liu, Juntao Dai, Xuehai Pan, Chi Zhang, Ce Bian, Ruiyang Sun, Yizhou Wang, and Yaodong Yang. 2023. Beavertails: Towards improved safety alignment of llm via a human-preference dataset. arXiv preprint arXiv:2307.04657 (2023).
[86] Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv preprint arXiv:2305.09645 (2023). | 2307.03109#153 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 154 | [87] Douglas Johnson, Rachel Goodman, J Patrinely, Cosby Stone, Eli Zimmerman, Rebecca Donald, Sam Chang, Sean Berkowitz, Avni Finn, Eiman Jahangir, et al. 2023. Assessing the accuracy and reliability of AI-generated medical responses: an evaluation of the Chat-GPT model. (2023).
[88] Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Vancouver, Canada. | 2307.03109#154 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 155 | [89] Saurav Kadavath, Tom Conerly, Amanda Askell, T. J. Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zachary Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, John Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom B. Brown, Jack Clark, Nicholas Joseph, Benjamin Mann, Sam McCandlish, Christopher Olah, and Jared Kaplan. 2022. Language Models (Mostly) Know What They Know. ArXiv abs/2207.05221 (2022). | 2307.03109#155 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 156 | [90] Ehud Karpas, Omri Abend, Yonatan Belinkov, Barak Lenz, Opher Lieber, Nir Ratner, Yoav Shoham, Hofit Bata, Yoav Levine, Kevin Leyton-Brown, et al. 2022. MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning. arXiv preprint arXiv:2205.00445 (2022). [91] Enkelejda Kasneci, Kathrin SeÃler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al. 2023. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences 103 (2023), 102274.
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
111:37
111:38
111:38
# Chang et al.
Chang et al.
[92] Jean Khalfa. 1994. What is intelligence? (1994). [93] Yousuf A Khan, Clarisse Hokia, Jennifer Xu, and Ben Ehlert. 2023. covLLM: Large Language Models for COVID-19 | 2307.03109#156 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 157 | Biomedical Literature. arXiv preprint arXiv:2306.04926 (2023).
[94] Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, et al. 2021. Dynabench: Rethinking benchmarking in NLP. arXiv preprint arXiv:2104.14337 (2021).
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[96] Stefan Kombrink, Tomas Mikolov, Martin Karafiát, and Lukás Burget. 2011. Recurrent Neural Network Based Language Modeling in Meeting Recognition.. In Interspeech, Vol. 11. 2877â2880. | 2307.03109#157 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[98] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019. Natural Questions: a Benchmark for Question Answering Research. Transactions of the Association of Computational Linguistics (2019).
[99] Adi Lahat, Eyal Shachar, Benjamin Avidan, Zina Shatz, Benjamin S Glicksberg, and Eyal Klang. 2023. Evaluating the use of large language model in identifying top research questions in gastroenterology. Scientific reports 13, 1 (2023), 4164. | 2307.03109#158 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 159 | [100] Viet Dac Lai, Nghia Trung Ngo, Amir Pouran Ben Veyseh, Hieu Man, Franck Dernoncourt, Trung Bui, and Thien Huu Nguyen. 2023. ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning. arXiv preprint arXiv:2304.05613 (2023).
[101] Pier Luca Lanzi and Daniele Loiacono. 2023. Chatgpt and other large language models as evolutionary engines for online interactive collaborative game design. arXiv preprint arXiv:2303.02155 (2023).
[102] Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, and Jimmy Xi- angji Huang. 2023. A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets. arXiv preprint arXiv:2305.18486 (2023).
[103] Van-Hoang Le and Hongyu Zhang. 2023. An Evaluation of Log Parsing with ChatGPT. arXiv preprint arXiv:2306.01590 (2023). | 2307.03109#159 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
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2307.03109 | 162 | [112] Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. AlpacaEval: An Automatic Evaluator of Instruction-following Models. https://github.com/tatsu- lab/alpaca_eval.
[113] Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, and Ji-Rong Wen. 2023. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355 (2023).
[114] Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2022. Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022). | 2307.03109#162 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 163 | [115] Tian Liang, Zhiwei He, Jen-tes Huang, Wenxuan Wang, Wenxiang Jiao, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi, and Xing Wang. 2023. Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models.
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# A Survey on Evaluation of Large Language Models
arXiv preprint arXiv:2310.20499 (2023).
[116] Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Zhaohui Wy, Dawei He, Peng Cheng, Zhonghao Wang, et al. 2023. UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation. arXiv preprint arXiv:2311.15296 (2023).
[117] Valentin Liévin, Christoffer Egeberg Hother, and Ole Winther. 2022. Can large language models reason about medical questions? arXiv preprint arXiv:2207.08143 (2022). | 2307.03109#163 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 165 | [122] Chuang Liu, Renren Jin, Yuqi Ren, Linhao Yu, Tianyu Dong, Xiaohan Peng, Shuting Zhang, Jianxiang Peng, Peiyi Zhang, Qingqing Lyu, Xiaowen Su, Qun Liu, and Deyi Xiong. 2023. M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models. arXiv:2305.10263 [cs.CL]
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 166 | [125] Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023. Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation. arXiv preprint arXiv:2305.01210 (2023).
[126] Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, and Dahua Lin. 2023. MMBench: Is Your Multi-modal Model an All-around Player? arXiv:2307.06281 [cs.CV]
[127] Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, et al. 2023. Summary of chatgpt/gpt-4 research and perspective towards the future of large language models. arXiv preprint arXiv:2304.01852 (2023). | 2307.03109#166 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 167 | [128] LMSYS. 2023. Chatbot Arena: Benchmarking LLMs in the Wild with Elo Ratings. https://lmsys.org. [129] Alejandro Lopez-Lira and Yuehua Tang. 2023. Can chatgpt forecast stock price movements? Return predictability
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[131] Qing Lyu, Josh Tan, Mike E Zapadka, Janardhana Ponnatapuram, Chuang Niu, Ge Wang, and Christopher T Whitlow. 2023. Translating radiology reports into plain language using chatgpt and gpt-4 with prompt learning: Promising results, limitations, and potential. arXiv preprint arXiv:2303.09038 (2023). | 2307.03109#167 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[133] Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv preprint arXiv:2303.08896 (2023).
[134] Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. 2023. MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization. arXiv:2301.12307 [cs.CL]
[135] Katerina Margatina, Shuai Wang, Yogarshi Vyas, Neha Anna John, Yassine Benajiba, and Miguel Ballesteros. 2023. Dynamic benchmarking of masked language models on temporal concept drift with multiple views. arXiv preprint arXiv:2302.12297 (2023). | 2307.03109#168 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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111:39
111:39
111:40
111:40
Chang et al. | 2307.03109#169 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 170 | J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
111:39
111:39
111:40
111:40
Chang et al.
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[142] Jekaterina Novikova, OndÅej DuÅ¡ek, Amanda Cercas Curry, and Verena Rieser. 2017. Why we need new evaluation metrics for NLG. arXiv preprint arXiv:1707.06875 (2017). | 2307.03109#170 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 171 | [143] Namkee Oh, Gyu-Seong Choi, and Woo Yong Lee. 2023. ChatGPT goes to the operating room: evaluating GPT-4 performance and its potential in surgical education and training in the era of large language models. Annals of Surgical Treatment and Research 104, 5 (2023), 269.
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in large language models using ChatGPT. Frontiers in Artificial Intelligence 6 (2023). | 2307.03109#171 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 173 | [151] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023. Unifying Large Language Models and Knowledge Graphs: A Roadmap. arXiv:2306.08302 [cs.CL]
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 174 | [155] Kaiping Peng, Richard E Nisbett, and Nancy YC Wong. 1997. Validity problems comparing values across cultures and possible solutions. Psychological methods 2, 4 (1997), 329.
[156] Pouya Pezeshkpour. 2023. Measuring and Modifying Factual Knowledge in Large Language Models. arXiv preprint arXiv:2306.06264 (2023).
[157] Jason Phang, Angelica Chen, William Huang, and Samuel R Bowman. 2021. Adversarially constructed evaluation sets are more challenging, but may not be fair. arXiv preprint arXiv:2111.08181 (2021).
[158] Dongqi Pu and Vera Demberg. 2023. ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer. arXiv:2306.07799 [cs.CL]
[159] Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is ChatGPT a general-purpose natural language processing task solver? arXiv preprint arXiv:2302.06476 (2023). | 2307.03109#174 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 175 | [160] Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2023. Tool Learning with Foundation Models. arXiv:2304.08354 [cs.CL] [161] Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, | 2307.03109#175 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 176 | Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, and Maosong Sun. 2023. ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs. arXiv:2307.16789 [cs.AI] [162] Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018. Improving language understanding by | 2307.03109#176 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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# A Survey on Evaluation of Large Language Models
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[166] Nicholas Riccardi and Rutvik H Desai. 2023. The Two Word Test: A Semantic Benchmark for Large Language Models. arXiv preprint arXiv:2306.04610 (2023). | 2307.03109#177 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 178 | [167] Jérôme Rutinowski, Sven Franke, Jan Endendyk, Ina Dormuth, and Markus Pauly. 2023. The Self-Perception and Political Biases of ChatGPT. arXiv preprint arXiv:2304.07333 (2023).
[168] Mustafa Safdari, Greg Serapio-GarcÃa, Clément Crepy, Stephen Fitz, Peter Romero, Luning Sun, Marwa Abdulhai, Aleksandra Faust, and Maja MatariÄ. 2023. Personality Traits in Large Language Models. arXiv preprint arXiv:2307.00184 (2023).
[169] Jamil S Samaan, Yee Hui Yeo, Nithya Rajeev, Lauren Hawley, Stuart Abel, Wee Han Ng, Nitin Srinivasan, Justin Park, Miguel Burch, Rabindra Watson, et al. 2023. Assessing the accuracy of responses by the language model ChatGPT to questions regarding bariatric surgery. Obesity Surgery (2023), 1â7. | 2307.03109#178 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 179 | [170] Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, and He He. 2023. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples. arXiv preprint arXiv:2305.15269 (2023).
[171] Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, and Aran Komatsuzaki. 2023. ARB: Advanced Reasoning Benchmark for Large Language Models. arXiv:2307.13692 [cs.CL]
[172] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 (2023). | 2307.03109#179 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 180 | [173] Prabin Sharma, Kisan Thapa, Prastab Dhakal, Mala Deep Upadhaya, Santosh Adhikari, and Salik Ram Khanal. 2023. Performance of ChatGPT on USMLE: Unlocking the Potential of Large Language Models for AI-Assisted Medical Education. arXiv preprint arXiv:2307.00112 (2023).
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identity. arXiv preprint arXiv:2209.12106 (2022). | 2307.03109#180 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 181 | identity. arXiv preprint arXiv:2209.12106 (2022).
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 182 | [179] Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. 2022. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv preprint arXiv:2201.11990 (2022). [180] Xiaoyang Song, Akshat Gupta, Kiyan Mohebbizadeh, Shujie Hu, and Anant Singh. 2023. Have Large Language Models Developed a Personality?: Applicability of Self-Assessment Tests in Measuring Personality in LLMs. arXiv preprint arXiv:2305.14693 (2023).
[181] Giriprasad Sridhara, Sourav Mazumdar, et al. 2023. ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks. arXiv preprint arXiv:2305.16837 (2023). | 2307.03109#182 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 183 | [182] Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615 (2022).
[183] Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023. Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent. arXiv preprint arXiv:2304.09542 (2023). | 2307.03109#183 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[186] Arun James Thirunavukarasu, Refaat Hassan, Shathar Mahmood, Rohan Sanghera, Kara Barzangi, Mohanned El Mukashfi, and Sachin Shah. 2023. Trialling a large language model (ChatGPT) in general practice with the Applied Knowledge Test: observational study demonstrating opportunities and limitations in primary care. JMIR Medical Education 9, 1 (2023), e46599. | 2307.03109#184 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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Abilities of Large Language ModelsâA Critical Investigation. arXiv preprint arXiv:2305.15771 (2023). | 2307.03109#186 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 187 | Abilities of Large Language ModelsâA Critical Investigation. arXiv preprint arXiv:2305.15771 (2023).
[195] Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. 2022. Large Language Models Still Canât Plan (A Benchmark for LLMs on Planning and Reasoning about Change). arXiv preprint arXiv:2206.10498 (2022).
[196] Chris Van Der Lee, Albert Gatt, Emiel Van Miltenburg, Sander Wubben, and Emiel Krahmer. 2019. Best practices for the human evaluation of automatically generated text. In Proceedings of the 12th International Conference on Natural Language Generation. 355â368.
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 188 | [198] Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, and Thang Luong. 2023. FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation. arXiv:2310.03214 [cs.CL]
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 189 | [200] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018). [201] Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li. 2023. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. arXiv:2306.11698 [cs.CL] | 2307.03109#189 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 190 | [202] Ben Wang and Aran Komatsuzaki. 2021. GPT-J-6B: A 6 billion parameter autoregressive language model. [203] Boxin Wang, Chejian Xu, Shuohang Wang, Zhe Gan, Yu Cheng, Jianfeng Gao, Ahmed Hassan Awadallah, and Bo Li. 2021. Adversarial glue: A multi-task benchmark for robustness evaluation of language models. arXiv preprint arXiv:2111.02840 (2021).
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[205] Hongru Wang, Rui Wang, Fei Mi, Zezhong Wang, Ruifeng Xu, and Kam-Fai Wong. 2023. Chain-of-thought prompting for responding to in-depth dialogue questions with LLM. arXiv:2305.11792 [cs.CL] | 2307.03109#190 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 191 | [206] Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al. 2023. On the robustness of chatgpt: An adversarial and out-of-distribution perspective. In ICLR workshop on Trustworthy and Reliable Large-Scale Machine Learning Models.
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J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
# A Survey on Evaluation of Large Language Models
[208] Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. 2023. Document- level machine translation with large language models. arXiv preprint arXiv:2304.02210 (2023). | 2307.03109#191 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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"id": "2306.05685"
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{
"id": "2306.05783"
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{
"id": "2201.08239"
},
{
"id": "2307.13692"
},
{
"id": "2307.02477"
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"id": "2306.05715"
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] |
2307.03109 | 192 | [209] Peiyi Wang, Lei Li, Liang Chen, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023. Large language models are not fair evaluators. arXiv preprint arXiv:2305.17926 (2023).
[210] Rose E Wang and Dorottya Demszky. 2023. Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction. arXiv preprint arXiv:2306.03090 (2023).
[211] Xidong Wang, Guiming Hardy Chen, Dingjie Song, Zhiyi Zhang, Zhihong Chen, Qingying Xiao, Feng Jiang, Jianquan Li, Xiang Wan, Benyou Wang, et al. 2023. CMB: A Comprehensive Medical Benchmark in Chinese. arXiv preprint arXiv:2308.08833 (2023).
[212] Xuena Wang, Xueting Li, Zi Yin, Yue Wu, and Liu Jia. 2023. Emotional Intelligence of Large Language Models. arXiv:2307.09042 [cs.AI] | 2307.03109#192 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 193 | [213] Xingyao Wang, Zihan Wang, Jiateng Liu, Yangyi Chen, Lifan Yuan, Hao Peng, and Heng Ji. 2023. MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback. arXiv preprint arXiv:2309.10691 (2023).
[214] Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. Codet5: Identifier-aware unified pre-trained encoder- decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 (2021).
[215] Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, and Shikun Zhang. | 2307.03109#193 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 194 | 2023. Exploring Vision-Language Models for Imbalanced Learning. arXiv preprint arXiv:2304.01457 (2023). [216] Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, et al. 2023. PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization. arXiv preprint arXiv:2306.05087 (2023).
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
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] |
2307.03109 | 197 | [226] Yuhuai Wu, Albert Qiaochu Jiang, Wenda Li, Markus Rabe, Charles Staats, Mateja Jamnik, and Christian Szegedy. 2022. Autoformalization with large language models. Advances in Neural Information Processing Systems 35 (2022), 32353â32368.
[227] Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim. 2023. Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks. arXiv preprint arXiv:2307.02477 (2023).
[228] Qiming Xie, Zengzhi Wang, Yi Feng, and Rui Xia. 2023. Ask Again, Then Fail: Large Language Modelsâ Vacillations in Judgement. arXiv:2310.02174 [cs.CL]
[229] Fangzhi Xu, Qika Lin, Jiawei Han, Tianzhe Zhao, Jun Liu, and Erik Cambria. 2023. Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation From Deductive, Inductive and Abductive Views. arXiv preprint arXiv:2306.09841 (2023). | 2307.03109#197 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 198 | [230] Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, and Jingren Zhou. 2023. CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility. arXiv:2307.09705 [cs.CL]
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[232] Ruiyun Xu, Yue Feng, and Hailiang Chen. 2023. ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience. arXiv preprint arXiv:2307.01135 (2023). | 2307.03109#198 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 199 | [233] Kai-Cheng Yang and Filippo Menczer. 2023. Large language models can rate news outlet credibility. arXiv preprint arXiv:2304.00228 (2023).
[234] Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, and Yue Zhang. 2022. Glue-x: Evaluating natural language understanding models from an out-of-distribution generalization perspective. arXiv preprint arXiv:2211.08073 (2022).
[235] Zhenfei Yin, Jiong Wang, Jianjian Cao, Zhelun Shi, Dingning Liu, Mukai Li, Lu Sheng, Lei Bai, Xiaoshui Huang, Zhiyong Wang, et al. 2023. LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark. arXiv preprint arXiv:2306.06687 (2023). | 2307.03109#199 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[237] Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu. 2023. MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. arXiv preprint arXiv:2309.12284 (2023). | 2307.03109#200 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 201 | [238] Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang. 2023. Mm-vet: Evaluating large multimodal models for integrated capabilities. arXiv preprint arXiv:2308.02490 (2023). [239] Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2023. Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations. arXiv:2306.04618 [cs.CL]
[240] Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, and Yongxin Ni. 2023. Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited. arXiv:2303.13835 [cs.IR] [241] Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, and Songfang Huang. 2023. How well do Large Language | 2307.03109#201 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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[245] Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, and Ji-Rong Wen. 2023. Evaluating | 2307.03109#202 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 203 | [245] Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, and Ji-Rong Wen. 2023. Evaluating
and Improving Tool-Augmented Computation-Intensive Math Reasoning. arXiv preprint arXiv:2306.02408 (2023).
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academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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{
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{
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},
{
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},
{
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{
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{
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] |
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[251] Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, and Lidong Bing. 2023. Sentiment Analysis in the Era of Large Language Models: A Reality Check. arXiv preprint arXiv:2305.15005 (2023). | 2307.03109#204 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 205 | [252] Xinghua Zhang, Bowen Yu, Haiyang Yu, Yangyu Lv, Tingwen Liu, Fei Huang, Hongbo Xu, and Yongbin Li. 2023. Wider and deeper llm networks are fairer llm evaluators. arXiv preprint arXiv:2308.01862 (2023).
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J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
# A Survey on Evaluation of Large Language Models
[254] Zhexin Zhang, Leqi Lei, Lindong Wu, Rui Sun, Yongkang Huang, Chong Long, Xiao Liu, Xuanyu Lei, Jie Tang, and Minlie Huang. 2023. SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions. arXiv preprint arXiv:2309.07045 (2023). | 2307.03109#205 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 206 | [255] Haozhe Zhao, Zefan Cai, Shuzheng Si, Xiaojian Ma, Kaikai An, Liang Chen, Zixuan Liu, Sheng Wang, Wenjuan Han, and Baobao Chang. 2023. MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning. arXiv preprint arXiv:2309.07915 (2023).
[256] Jiaxu Zhao, Meng Fang, Zijing Shi, Yitong Li, Ling Chen, and Mykola Pechenizkiy. 2023. CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models. arXiv:2305.11262 [cs.CL]
[257] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023). [258] Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, and Min Lin. 2023. On Evaluating | 2307.03109#206 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 207 | Adversarial Robustness of Large Vision-Language Models. arXiv preprint arXiv:2305.16934 (2023).
[259] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Tianle Li, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zhuohan Li, Zi Lin, Eric Xing, et al. 2023. LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset. arXiv preprint arXiv:2309.11998 (2023).
[260] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging LLM-as-a-judge with MT-Bench and Chatbot Arena. arXiv:2306.05685 [cs.CL] | 2307.03109#207 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 208 | [261] Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022. Towards a unified multi-dimensional evaluator for text generation. arXiv preprint arXiv:2210.07197 (2022).
[262] Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. 2023. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364 (2023).
[263] Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022. Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910 (2022). | 2307.03109#208 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.03109 | 209 | [264] Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, et al. 2023. PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts. arXiv preprint arXiv:2306.04528 (2023).
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[266] Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing. 2023. Exploring ai ethics of chatgpt: A diagnostic analysis. arXiv preprint arXiv:2301.12867 (2023). | 2307.03109#209 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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] |
2307.03109 | 210 | [267] Terry Yue Zhuo, Zhuang Li, Yujin Huang, Yuan-Fang Li, Weiqing Wang, Gholamreza Haffari, and Fatemeh Shiri. 2023. On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codex. arXiv preprint arXiv:2301.12868 (2023).
[268] Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019). [269] Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2023. Can Large Language
Models Transform Computational Social Science? arXiv preprint arXiv:2305.03514 (2023).
Received 20 February 2007; revised 12 March 2009; accepted 5 June 2009
J. ACM, Vol. 37, No. 4, Article 111. Publication date: August 2018.
111:45 | 2307.03109#210 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at the society level for better understanding of their
potential risks. Over the past years, significant efforts have been made to
examine LLMs from various perspectives. This paper presents a comprehensive
review of these evaluation methods for LLMs, focusing on three key dimensions:
what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide
an overview from the perspective of evaluation tasks, encompassing general
natural language processing tasks, reasoning, medical usage, ethics,
educations, natural and social sciences, agent applications, and other areas.
Secondly, we answer the `where' and `how' questions by diving into the
evaluation methods and benchmarks, which serve as crucial components in
assessing performance of LLMs. Then, we summarize the success and failure cases
of LLMs in different tasks. Finally, we shed light on several future challenges
that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to
researchers in the realm of LLMs evaluation, thereby aiding the development of
more proficient LLMs. Our key point is that evaluation should be treated as an
essential discipline to better assist the development of LLMs. We consistently
maintain the related open-source materials at:
https://github.com/MLGroupJLU/LLM-eval-survey. | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | [
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2307.02046 | 1 | AbstractâWith the prosperity of e-commerce and web applications, Recommender Systems (RecSys) have become an important component in our daily life, providing personalized suggestions that cater to user preferences. While Deep Neural Networks (DNNs) have made significant advancements in enhancing recommender systems by modeling user-item interactions and incorporating their textual side information, these DNN-based methods still have some limitations, such as difficulties in effectively understanding usersâ interests and capturing textual side information, inabilities in generalizing to various seen/unseen recommendation scenarios and reasoning on their predictions, etc. Meanwhile, the emergence of Large Language Models (LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI), due to their remarkable abilities in fundamental responsibilities of language understanding and generation, as well as impressive generalization and reasoning capabilities. As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing | 2307.02046#1 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling user-item interactions and incorporating textual side
information, DNN-based methods still face limitations, such as difficulties in
understanding users' interests and capturing textual side information,
inabilities in generalizing to various recommendation scenarios and reasoning
on their predictions, etc. Meanwhile, the emergence of Large Language Models
(LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural
Language Processing (NLP) and Artificial Intelligence (AI), due to their
remarkable abilities in fundamental responsibilities of language understanding
and generation, as well as impressive generalization and reasoning
capabilities. As a result, recent studies have attempted to harness the power
of LLMs to enhance recommender systems. Given the rapid evolution of this
research direction in recommender systems, there is a pressing need for a
systematic overview that summarizes existing LLM-empowered recommender systems,
to provide researchers in relevant fields with an in-depth understanding.
Therefore, in this paper, we conduct a comprehensive review of LLM-empowered
recommender systems from various aspects including Pre-training, Fine-tuning,
and Prompting. More specifically, we first introduce representative methods to
harness the power of LLMs (as a feature encoder) for learning representations
of users and items. Then, we review recent techniques of LLMs for enhancing
recommender systems from three paradigms, namely pre-training, fine-tuning, and
prompting. Finally, we comprehensively discuss future directions in this
emerging field. | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | [
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2307.02477 | 1 | Language Models Through Counterfactual Tasks Zhaofeng Wu® ~~ Linlu Qiu® ~â Alexis Ross® = Ekin Akyiirek® Boyuan Chen® Bailin Wang® Najoung Kim? Jacob Andreas® Yoon Kim® ®MIT âBoston University [email protected] Arithmetic Code Exec. Code Gen. Basic Syntax Logic random ; : : : 2 GPT-4 as as as Lee! ieee) Performance = = = Lee eee 0 100 AR SrsSDS, ba"), Sort list by the Find the main IfX are Y,Y are Z. 5 keyetéabda x} xâ¬x1, second element subject and verb Are XZ? in base-10 in Python in Python âThey think LMs are X= corgis Default the bestâ in Y= sorted subj-verb-obj order Tis 3 pei x: xf], 6 a [baâ, âabâ 5 (they, think) Yes Counterfactual in base-9 w/1-based indexing w/1-based indexing iets ates Verb-obj-subj order Z= plants - @ list, keyslonbda x: x{21, âa DD 100 [âabâ, âbaâ] eye (they, think) | 2307.02477#1 | Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks | The impressive performance of recent language models across a wide range of
tasks suggests that they possess a degree of abstract reasoning skills. Are
these skills general and transferable, or specialized to specific tasks seen
during pretraining? To disentangle these effects, we propose an evaluation
framework based on "counterfactual" task variants that deviate from the default
assumptions underlying standard tasks. Across a suite of 11 tasks, we observe
nontrivial performance on the counterfactual variants, but nevertheless find
that performance substantially and consistently degrades compared to the
default conditions. This suggests that while current LMs may possess abstract
task-solving skills to a degree, they often also rely on narrow,
non-transferable procedures for task-solving. These results motivate a more
careful interpretation of language model performance that teases apart these
aspects of behavior. | http://arxiv.org/pdf/2307.02477 | Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230801 | [] |
2307.02485 | 1 | 1University of Massachusetts Amherst, 2 Tsinghua University, 3Shanghai Jiao Tong University, 4MIT, 5MIT-IBM Watson AI Lab
# Abstract
Large Language Models (LLMs) have demonstrated impressive planning abilities in single-agent embodied tasks across various domains. However, their capacity for planning and communication in multi-agent cooperation remains unclear, even though these are crucial skills for intelligent embodied agents. In this paper, we present a novel framework that utilizes LLMs for multi-agent cooperation and tests it in various embodied environments. Our framework enables embodied agents to plan, communicate, and cooperate with other embodied agents or humans to accomplish long-horizon tasks efï¬ciently. We demonstrate that recent LLMs, such as GPT-4, can surpass strong planning-based methods and exhibit emergent effective communication using our framework without requiring ï¬ne-tuning or few-shot prompting. We also discover that LLM-based agents that communicate in natural language can earn more trust and cooperate more effectively with humans. Our research underscores the potential of LLMs for embodied AI and lays the foundation for future research in multi-agent cooperation. Videos can be found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/.
# Introduction | 2307.02485#1 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | [
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2307.02486 | 1 | # Abstract
Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted. To address this issue, we introduce LONGNET, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacriï¬cing the performance on shorter sequences. Speciï¬cally, we propose dilated attention, which expands the attentive ï¬eld exponentially as the distance grows. LONGNET has signiï¬cant advantages: 1) it has a linear computation complexity and a logarithm depen- dency between any two tokens in a sequence; 2) it can be served as a distributed trainer for extremely long sequences; 3) its dilated attention is a drop-in replace- ment for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization. Experiments results demonstrate that LONGNET yields strong performance on both long-sequence modeling and general language tasks. Our work opens up new possibilities for modeling very long sequences, e.g., treating a whole corpus or even the entire Internet as a sequence. Code is available at https://aka.ms/LongNet. | 2307.02486#1 | LongNet: Scaling Transformers to 1,000,000,000 Tokens | Scaling sequence length has become a critical demand in the era of large
language models. However, existing methods struggle with either computational
complexity or model expressivity, rendering the maximum sequence length
restricted. To address this issue, we introduce LongNet, a Transformer variant
that can scale sequence length to more than 1 billion tokens, without
sacrificing the performance on shorter sequences. Specifically, we propose
dilated attention, which expands the attentive field exponentially as the
distance grows. LongNet has significant advantages: 1) it has a linear
computation complexity and a logarithm dependency between any two tokens in a
sequence; 2) it can be served as a distributed trainer for extremely long
sequences; 3) its dilated attention is a drop-in replacement for standard
attention, which can be seamlessly integrated with the existing
Transformer-based optimization. Experiments results demonstrate that LongNet
yields strong performance on both long-sequence modeling and general language
tasks. Our work opens up new possibilities for modeling very long sequences,
e.g., treating a whole corpus or even the entire Internet as a sequence. | http://arxiv.org/pdf/2307.02486 | Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, Furu Wei | cs.CL, cs.LG | Work in progress | null | cs.CL | 20230705 | 20230719 | [] |
2307.03692 | 1 | Parikshith Kulkarni
Melisa Russak
Writer, Inc. {waseem,...,melisa}@writer.com
# Abstract
In this paper, we introduce the Instruction Following Score (IFS), a metric that detects language modelsâ ability to follow instructions. The metric has a dual purpose. First, IFS can be used to distinguish between base and instruct models. We benchmark publicly available base and instruct models, and show that the ratio of well formatted responses to partial and full sentences can be an effective measure between those two model classes. Secondly, the metric can be used as an early stopping criteria for instruct tuning. We compute IFS for Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models learn to follow instructions relatively early in the training process, and the further ï¬netuning can result in changes in the underlying base model semantics. As an example of semantics change we show the objectivity of model predictions, as deï¬ned by an auxiliary metric ObjecQA. We show that in this particular case, semantic changes are the steepest when the IFS tends to plateau. We hope that decomposing instruct tuning into IFS and semantic factors starts a new trend in better controllable instruct tuning and opens possibilities for designing minimal instruct interfaces querying foundation models.
# Introduction
which result in a plethora of possible combina- tions leading to distinct instruct models. | 2307.03692#1 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the ratio of well formatted responses to partial and full sentences can be an
effective measure between those two model classes. Secondly, the metric can be
used as an early stopping criteria for instruct tuning. We compute IFS for
Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
learn to follow instructions relatively early in the training process, and the
further finetuning can result in changes in the underlying base model
semantics. As an example of semantics change we show the objectivity of model
predictions, as defined by an auxiliary metric ObjecQA. We show that in this
particular case, semantic changes are the steepest when the IFS tends to
plateau. We hope that decomposing instruct tuning into IFS and semantic factors
starts a new trend in better controllable instruct tuning and opens
possibilities for designing minimal instruct interfaces querying foundation
models. | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | [
{
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2307.02046 | 2 | enhance recommender systems. Given the rapid evolution of this research direction in recommender systems, there is a pressing need for a systematic overview that summarizes existing LLM-empowered recommender systems, so as to provide researchers and practitioners in relevant fields with an in-depth understanding. Therefore, in this survey, we conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting. More specifically, we first introduce representative methods to harness the power of LLMs (as a feature encoder) for learning representations of users and items. Then, we review recent advanced techniques of LLMs for enhancing recommender systems from three paradigms, namely pre-training, fine-tuning, and prompting. Finally, we comprehensively discuss the promising future directions in this emerging field. | 2307.02046#2 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling user-item interactions and incorporating textual side
information, DNN-based methods still face limitations, such as difficulties in
understanding users' interests and capturing textual side information,
inabilities in generalizing to various recommendation scenarios and reasoning
on their predictions, etc. Meanwhile, the emergence of Large Language Models
(LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural
Language Processing (NLP) and Artificial Intelligence (AI), due to their
remarkable abilities in fundamental responsibilities of language understanding
and generation, as well as impressive generalization and reasoning
capabilities. As a result, recent studies have attempted to harness the power
of LLMs to enhance recommender systems. Given the rapid evolution of this
research direction in recommender systems, there is a pressing need for a
systematic overview that summarizes existing LLM-empowered recommender systems,
to provide researchers in relevant fields with an in-depth understanding.
Therefore, in this paper, we conduct a comprehensive review of LLM-empowered
recommender systems from various aspects including Pre-training, Fine-tuning,
and Prompting. More specifically, we first introduce representative methods to
harness the power of LLMs (as a feature encoder) for learning representations
of users and items. Then, we review recent techniques of LLMs for enhancing
recommender systems from three paradigms, namely pre-training, fine-tuning, and
prompting. Finally, we comprehensively discuss future directions in this
emerging field. | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | [
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{
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{
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{
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{
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},
{
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2307.02053 | 2 | # Abstract
Recently, the release of INSTRUCTEVAL [Chia et al., 2023] has provided valuable insights into the performance of large language models (LLMs) that utilize encoder- decoder or decoder-only architecture. Interestingly, despite being introduced four years ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general problem-solving skills. This performance discrepancy can be attributed to three key factors: (1) Pre-training data, (2) Backbone architecture, and (3) Instruction dataset. In this technical report, our main focus is on investigating the impact of the third factor by leveraging VICUNA, a large language model based on LLAMA, which has undergone fine-tuning on ChatGPT conversations. To achieve this objective, we fine-tuned VICUNA using a customized instruction dataset collection called FLAN-MINI. This collection includes a subset of the large-scale instruction dataset known as FLAN, as well as various code-related datasets and conversational datasets derived from ChatGPT/GPT-4. This dataset comprises a large number of tasks that
Preprint. Under review. | 2307.02053#2 | Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning | Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general
problem-solving skills. This performance discrepancy can be attributed to three
key factors: (1) Pre-training data, (2) Backbone architecture, and (3)
Instruction dataset. In this technical report, our main focus is on
investigating the impact of the third factor by leveraging VICUNA, a large
language model based on LLAMA, which has undergone fine-tuning on ChatGPT
conversations. To achieve this objective, we fine-tuned VICUNA using a
customized instruction dataset collection called FLANMINI. This collection
includes a subset of the large-scale instruction dataset known as FLAN, as well
as various code-related datasets and conversational datasets derived from
ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand
problem-solving skills. Our experimental findings strongly indicate that the
enhanced problem-solving abilities of our model, FLACUNA, are obtained through
fine-tuning VICUNA on the FLAN dataset, leading to significant improvements
across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly
available at https://huggingface.co/declare-lab/flacuna-13b-v1.0. | http://arxiv.org/pdf/2307.02053 | Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria | cs.CL | null | null | cs.CL | 20230705 | 20230705 | [
{
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2307.02477 | 2 | plants - @ list, keyslonbda x: x{21, âa DD 100 [âabâ, âbaâ] eye (they, think) Yes Spatial Drawing Chord Fingering Note in Melody Chess SET Game H = 2 Lee ees Li eee , ; sor tri The 4th note of le = 2 Coordinates of Draw a bubble tea Play C major triad Pear Is the move legal? Ie S N ona guitar in C major st Rule: Either identi- : : a 4 cal or all different in 3 Boe color, number, shape, & shading Wee th prea Gi s ai ii fe azaeos (0) les i Yes Yes N rotated 180° with A string tuned in A> major re Rule (additional): toaCand D toanF ADA Except, for number 2 Wa h-E perin rr cards should be the (** tr) same while 1 differs (4,0) . é E i No No | 2307.02477#2 | Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks | The impressive performance of recent language models across a wide range of
tasks suggests that they possess a degree of abstract reasoning skills. Are
these skills general and transferable, or specialized to specific tasks seen
during pretraining? To disentangle these effects, we propose an evaluation
framework based on "counterfactual" task variants that deviate from the default
assumptions underlying standard tasks. Across a suite of 11 tasks, we observe
nontrivial performance on the counterfactual variants, but nevertheless find
that performance substantially and consistently degrades compared to the
default conditions. This suggests that while current LMs may possess abstract
task-solving skills to a degree, they often also rely on narrow,
non-transferable procedures for task-solving. These results motivate a more
careful interpretation of language model performance that teases apart these
aspects of behavior. | http://arxiv.org/pdf/2307.02477 | Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230801 | [] |
2307.02485 | 2 | # Introduction
Large Language Models (LLMs) have exhibited remarkable capabilities across various domains, implying their mastery of natural language understanding, rich world knowledge, and complex reasoning capability[6]. Recent research has also demonstrated that LLMs can function as planners in single-agent embodied tasks through zero-shot prompting for instruction following tasks [15] or few-shot prompting for more complex long-horizon tasks [44]. However, for embodied agents to work with other agents or with humans, they also need to have strong abilities for cooperation and communication. To date, it still remains unclear whether LLMs have such abilities necessary for embodied multi-agent cooperation.
Therefore, this paper aims to investigate whether LLMs can help build cooperative embodied agents that can collaborate with other agents and humans to accomplish complex tasks through collaborative planning and communication. To this end, we focus on an embodied multi-agent setting as shown in Figure 1, where two embodied agents have to cooperate to ï¬nish a task as soon as possible. To succeed in this setting, agents must i) extract useful information from observations, ii) revise their beliefs about the world and other agents, iii) decide what and when to communicate, and iv) plan collaboratively to reach the common goal.
To achieve these goals, we introduce a novel framework that utilizes LLMs to plan and communicate with other agents to cooperatively solve complex embodied tasks without any ï¬ne-tuning or few-shot
denotes equal contribution. | 2307.02485#2 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | [
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2307.03692 | 2 | # Introduction
which result in a plethora of possible combina- tions leading to distinct instruct models.
Large Language Models (LLMs) ï¬netuned on in- struct data can behave like conversational agents (Alpaca: Taori et al. 2023, Self-Instruct: Wang et al. 2023). The recipe for a chat model is well- deï¬ned: one needs to perform instruction tuning, which means supervised ï¬netuning (SFT) of an LLM on tuples of instruction and response (Long- pre et al. 2023).
Open-source datasets vary in quality and quantity, ranging from 1k examples (Zhou et al. 2023) to over 800k examples (Anand et al. 2023). In addi- tion, there are more than a dozen open-source base LLMs, such as LLaMA (Touvron et al. 2023), OPT (Zhang et al. 2022), GPT-Neo (Gao et al. 2020), Palmyra (Writer 2023), and others, | 2307.03692#2 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the ratio of well formatted responses to partial and full sentences can be an
effective measure between those two model classes. Secondly, the metric can be
used as an early stopping criteria for instruct tuning. We compute IFS for
Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
learn to follow instructions relatively early in the training process, and the
further finetuning can result in changes in the underlying base model
semantics. As an example of semantics change we show the objectivity of model
predictions, as defined by an auxiliary metric ObjecQA. We show that in this
particular case, semantic changes are the steepest when the IFS tends to
plateau. We hope that decomposing instruct tuning into IFS and semantic factors
starts a new trend in better controllable instruct tuning and opens
possibilities for designing minimal instruct interfaces querying foundation
models. | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | [
{
"id": "2101.00027"
}
] |
2307.02046 | 3 | Index TermsâRecommender Systems, Large Language Models (LLMs), Pre-training and Fine-tuning, In-context Learning, Prompting. â¦
# 1 INTRODUCTION
alleviating information overload for enriching usersâ online experience (i.e., users need to filter overwhelming information to locate their interested information) [1], [2]. They offer personalized suggestions towards candidate items tailored to meet user preferences in various application domains, such as entertainment [3], e-commerce [4], and job matching [2]. For example, on movie recommendations (e.g., IMDB and Netflix), the latest movies are recommended to users based on the content of movies and the past interaction histories of users, which help users discover new movies that accord with their interests. The basic idea of recommender systems is to make use of the interactions between users and items and their associated side information, especially textual information (e.g., item titles or descriptions, user profiles, and user reviews for items), to predict the matching | 2307.02046#3 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling user-item interactions and incorporating textual side
information, DNN-based methods still face limitations, such as difficulties in
understanding users' interests and capturing textual side information,
inabilities in generalizing to various recommendation scenarios and reasoning
on their predictions, etc. Meanwhile, the emergence of Large Language Models
(LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural
Language Processing (NLP) and Artificial Intelligence (AI), due to their
remarkable abilities in fundamental responsibilities of language understanding
and generation, as well as impressive generalization and reasoning
capabilities. As a result, recent studies have attempted to harness the power
of LLMs to enhance recommender systems. Given the rapid evolution of this
research direction in recommender systems, there is a pressing need for a
systematic overview that summarizes existing LLM-empowered recommender systems,
to provide researchers in relevant fields with an in-depth understanding.
Therefore, in this paper, we conduct a comprehensive review of LLM-empowered
recommender systems from various aspects including Pre-training, Fine-tuning,
and Prompting. More specifically, we first introduce representative methods to
harness the power of LLMs (as a feature encoder) for learning representations
of users and items. Then, we review recent techniques of LLMs for enhancing
recommender systems from three paradigms, namely pre-training, fine-tuning, and
prompting. Finally, we comprehensively discuss future directions in this
emerging field. | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | [
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2307.02053 | 3 | Preprint. Under review.
demand problem-solving skills. Our experimental findings strongly indicate that the enhanced problem-solving abilities of our model, FLACUNA, are obtained through fine-tuning VICUNA on the FLAN dataset, leading to significant improvements across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly available at https://huggingface.co/declare-lab/flacuna-13b-v1.0.
# 1 Introduction | 2307.02053#3 | Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning | Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general
problem-solving skills. This performance discrepancy can be attributed to three
key factors: (1) Pre-training data, (2) Backbone architecture, and (3)
Instruction dataset. In this technical report, our main focus is on
investigating the impact of the third factor by leveraging VICUNA, a large
language model based on LLAMA, which has undergone fine-tuning on ChatGPT
conversations. To achieve this objective, we fine-tuned VICUNA using a
customized instruction dataset collection called FLANMINI. This collection
includes a subset of the large-scale instruction dataset known as FLAN, as well
as various code-related datasets and conversational datasets derived from
ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand
problem-solving skills. Our experimental findings strongly indicate that the
enhanced problem-solving abilities of our model, FLACUNA, are obtained through
fine-tuning VICUNA on the FLAN dataset, leading to significant improvements
across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly
available at https://huggingface.co/declare-lab/flacuna-13b-v1.0. | http://arxiv.org/pdf/2307.02053 | Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria | cs.CL | null | null | cs.CL | 20230705 | 20230705 | [
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2307.02477 | 3 | Figure 1: GPT-4âs performance on the default version of various tasks (blue) and counterfactual counterparts (orange). The shown results use 0-shot chain-of-thought prompting (§4; Kojima et al., 2023). GPT-4 consistently and substantially underperforms on counterfactual variants compared to default task instantiations.
# Abstract
# Abstract
The impressive performance of recent lan- guage models across a wide range of tasks suggests that they possess a degree of ab- stract reasoning skills. Are these skills gen- eral and transferable, or specialized to spe- cific tasks seen during pretraining? To dis- entangle these effects, we propose an eval- uation framework based on âcounterfactualâ task variants that deviate from the default as- sumptions underlying standard tasks. Across
a suite of 11 tasks, we observe nontrivial per- formance on the counterfactual variants, but nevertheless find that performance substan- tially and consistently degrades compared to the default conditions. This suggests that while current LMs may possess abstract task- solving skills to a degree, they often also rely on narrow, non-transferable procedures for task-solving. These results motivate a more careful interpretation of language model per- formance that teases apart these aspects of behavior.
# Introduction | 2307.02477#3 | Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks | The impressive performance of recent language models across a wide range of
tasks suggests that they possess a degree of abstract reasoning skills. Are
these skills general and transferable, or specialized to specific tasks seen
during pretraining? To disentangle these effects, we propose an evaluation
framework based on "counterfactual" task variants that deviate from the default
assumptions underlying standard tasks. Across a suite of 11 tasks, we observe
nontrivial performance on the counterfactual variants, but nevertheless find
that performance substantially and consistently degrades compared to the
default conditions. This suggests that while current LMs may possess abstract
task-solving skills to a degree, they often also rely on narrow,
non-transferable procedures for task-solving. These results motivate a more
careful interpretation of language model performance that teases apart these
aspects of behavior. | http://arxiv.org/pdf/2307.02477 | Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230801 | [] |
2307.02485 | 3 | denotes equal contribution.
Preprint. Under review.
Alice CL] âHi, Bob. I L_ | found 3 target objects: fl in the kitchen, a Tremember you were holding an om empty container, Orange Apple can you come here to wera pick them up while I Po go to explore other imal rooms?â
Figure 1: We aim to utilize Large Language Models to build cooperative embodied agents.
prompting. Our framework consists of ï¬ve modules, each to address a critical aspect of successful multi-agent cooperation, including a belief module to monitor the agentâs understanding of both the physical environment and other agents, a communication module to decide what to communicate utilizing the strong free-form dialogue generation and understanding capability of LLMs, and a reasoning module to synthesize all the information provided by other modules to decide high-level plans including when to communicate. | 2307.02485#3 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | [
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2307.02486 | 3 | # Millions
$ < 5 a
Figure 1: Trend of Transformer sequence lengths over time.
â
Equal contribution. â Corresponding author.
2024
# 1 Introduction
+
+
20, ZKHB22, Recent years have witnessed a trend toward scaling neural networks [BMR 23]. The depth is primarily scaled up for exponential expressivity, producing CND 22]. Then, the sparse MoE mod- many powerful deep networks [HZRS16, HCB els [LLX 22] efï¬ciently enlarge the hidden dimension. Sequence length, as the last atomic dimension of the neural net- work, is desirable to be unlimited. Breaking the limitation of sequence length introduces signiï¬cant advantages. First, it provides large memory and receptive ï¬eld for models, which is practical for them to interact with human and the world. Second, a longer context contains more complex causality and reasoning paths that models can exploit in training data. In contrast, short dependency has more spurious correlations, which is harmful to generalization. Third, it enables to explore the limits of in-context learning, which has the potential to be a paradigm shift for many-shot learning, as an extremely long context may help the models alleviate catastrophic forgetting. | 2307.02486#3 | LongNet: Scaling Transformers to 1,000,000,000 Tokens | Scaling sequence length has become a critical demand in the era of large
language models. However, existing methods struggle with either computational
complexity or model expressivity, rendering the maximum sequence length
restricted. To address this issue, we introduce LongNet, a Transformer variant
that can scale sequence length to more than 1 billion tokens, without
sacrificing the performance on shorter sequences. Specifically, we propose
dilated attention, which expands the attentive field exponentially as the
distance grows. LongNet has significant advantages: 1) it has a linear
computation complexity and a logarithm dependency between any two tokens in a
sequence; 2) it can be served as a distributed trainer for extremely long
sequences; 3) its dilated attention is a drop-in replacement for standard
attention, which can be seamlessly integrated with the existing
Transformer-based optimization. Experiments results demonstrate that LongNet
yields strong performance on both long-sequence modeling and general language
tasks. Our work opens up new possibilities for modeling very long sequences,
e.g., treating a whole corpus or even the entire Internet as a sequence. | http://arxiv.org/pdf/2307.02486 | Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, Furu Wei | cs.CL, cs.LG | Work in progress | null | cs.CL | 20230705 | 20230719 | [] |
2307.03692 | 3 | We can see instruct tuning attempts through the lens of the "imitation models" - concept intro- duced by Gudibande et al. 2023, i.e., efforts to distil closed (and possibly much bigger) propri- etary models like ChatGPT (OpenAI 2022), Bard (Pichai 2023), and Claude (AnthropicAI 2023).
Little is known about the qualitative impact of the distillation process on the base model (Hin- ton, Vinyals, and Dean 2015). Imitation success is measured in terms of knowledge (e.g., HELM Liang et al. 2022), skills (e.g., Natural Questions Kwiatkowski et al. 2019) or manual checks based on human preferences (Zhou et al. 2023). There is no consensus whether a manual check that might skew the metric towards style and formatting of responses is a good overall metric (Gudibande
Preprint. Under review.
et al. 2023). A fairly recent attempt to more ro- bustly evaluate instruct models is the Hugging- face Leaderboard (Huggingface 2023b), which evaluates models against four key benchmarks from the Eleuther AI Language Model Evalua- tion Harness (Gao et al. 2021). | 2307.03692#3 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the ratio of well formatted responses to partial and full sentences can be an
effective measure between those two model classes. Secondly, the metric can be
used as an early stopping criteria for instruct tuning. We compute IFS for
Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
learn to follow instructions relatively early in the training process, and the
further finetuning can result in changes in the underlying base model
semantics. As an example of semantics change we show the objectivity of model
predictions, as defined by an auxiliary metric ObjecQA. We show that in this
particular case, semantic changes are the steepest when the IFS tends to
plateau. We hope that decomposing instruct tuning into IFS and semantic factors
starts a new trend in better controllable instruct tuning and opens
possibilities for designing minimal instruct interfaces querying foundation
models. | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | [
{
"id": "2101.00027"
}
] |
2307.02046 | 4 | ⢠W. Fan, Z. Zhao, J. Li, Y. Liu, and Q. Li are with the Department of Computing, The Hong Kong Polytechnic University. E-mail: {wenqifan03, scofield.zzh}@gmail.com, {jiatong.li, yunqing617.liu}@connect.polyu.hk, [email protected].
⢠X. Mei is with the Department of Management and Marketing, The Hong Kong Polytechnic University. E-mail: [email protected].
⢠Y. Wang is with National University of Defense Technology. E-mail: [email protected].
score between users and items (i.e., the probability that the user would like the item) [5]. More specifically, collaborative behaviors between users and items have been leveraged to design various recommendation models, which can be further used to learn the representations of users and items [6], [7]. In addition, textual side information about users and items contains rich knowledge that can assist in the calculation of the matching scores, providing great opportunities to understand user preferences for advancing recommender systems [8]. | 2307.02046#4 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling user-item interactions and incorporating textual side
information, DNN-based methods still face limitations, such as difficulties in
understanding users' interests and capturing textual side information,
inabilities in generalizing to various recommendation scenarios and reasoning
on their predictions, etc. Meanwhile, the emergence of Large Language Models
(LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural
Language Processing (NLP) and Artificial Intelligence (AI), due to their
remarkable abilities in fundamental responsibilities of language understanding
and generation, as well as impressive generalization and reasoning
capabilities. As a result, recent studies have attempted to harness the power
of LLMs to enhance recommender systems. Given the rapid evolution of this
research direction in recommender systems, there is a pressing need for a
systematic overview that summarizes existing LLM-empowered recommender systems,
to provide researchers in relevant fields with an in-depth understanding.
Therefore, in this paper, we conduct a comprehensive review of LLM-empowered
recommender systems from various aspects including Pre-training, Fine-tuning,
and Prompting. More specifically, we first introduce representative methods to
harness the power of LLMs (as a feature encoder) for learning representations
of users and items. Then, we review recent techniques of LLMs for enhancing
recommender systems from three paradigms, namely pre-training, fine-tuning, and
prompting. Finally, we comprehensively discuss future directions in this
emerging field. | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | [
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2307.02053 | 4 | ChatGPT and its successor GPT-4 have surpassed their prior state-of-the-art models on a vast majority of the benchmarking tasks and datasets. However, to preserve privacy, natively running a 175B+ sized model like GPT-3 is beyond the capabilities of most organizations, let alone individuals. This has prompted many researchers to fine-tune manageable-sized LLMs â from 7B to 30B on a diverse set of instruction examples generated by ChatGPT or GPT-4. This has birthed LLMs, such as, Alpaca [Taori et al., 2023] and VICUNA [Chiang et al., 2023] that are fine-tuned checkpoints of LLaMA [Touvron et al., 2023]. These models have attained close to ChatGPT-level performance on some specific benchmarking tasks, but overall generalization still remains elusive. Recent works like INSTRUCTEVAL [Chia et al., 2023] strongly hint that the fine-tuning datasets dictate the task-specific performances. For instance, it has been observed that FLAN-T5 â a T5 checkpoint fine-tuned on FLAN Collection instruction dataset â | 2307.02053#4 | Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning | Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general
problem-solving skills. This performance discrepancy can be attributed to three
key factors: (1) Pre-training data, (2) Backbone architecture, and (3)
Instruction dataset. In this technical report, our main focus is on
investigating the impact of the third factor by leveraging VICUNA, a large
language model based on LLAMA, which has undergone fine-tuning on ChatGPT
conversations. To achieve this objective, we fine-tuned VICUNA using a
customized instruction dataset collection called FLANMINI. This collection
includes a subset of the large-scale instruction dataset known as FLAN, as well
as various code-related datasets and conversational datasets derived from
ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand
problem-solving skills. Our experimental findings strongly indicate that the
enhanced problem-solving abilities of our model, FLACUNA, are obtained through
fine-tuning VICUNA on the FLAN dataset, leading to significant improvements
across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly
available at https://huggingface.co/declare-lab/flacuna-13b-v1.0. | http://arxiv.org/pdf/2307.02053 | Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria | cs.CL | null | null | cs.CL | 20230705 | 20230705 | [
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2307.02477 | 4 | # Introduction
The striking empirical successes of language mod- els (LMs) suggest that next-word prediction at scale may be a viable approach for distilling the knowl- edge embedded in large-scale text corpora into general-purpose interactive agents. LMs obtain im- pressive results on various NLP benchmarks (Ope- nAI, 2023; Anil et al., 2023; Anthropic, 2023; i.a.) and display surprising abilities that suggest a non- trivial understanding of the world (Bubeck et al., 2023). They have been shown to pass professional exams (Kung et al., 2023; Nori et al., 2023; Terwi- esch, 2023; i.a.), exceed state-of-the-art methods on many traditional benchmarks (Sun et al., 2023; Sobania et al., 2023; Zhang et al., 2023a; Dhingra et al., 2023; i.a.), and surpass human performance on tasks that require seemingly nontrivial reason- ing (Chowdhery et al., 2022; Hoffmann et al., 2022; Malinka et al., 2023; Guo et al., 2023; i.a.). | 2307.02477#4 | Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks | The impressive performance of recent language models across a wide range of
tasks suggests that they possess a degree of abstract reasoning skills. Are
these skills general and transferable, or specialized to specific tasks seen
during pretraining? To disentangle these effects, we propose an evaluation
framework based on "counterfactual" task variants that deviate from the default
assumptions underlying standard tasks. Across a suite of 11 tasks, we observe
nontrivial performance on the counterfactual variants, but nevertheless find
that performance substantially and consistently degrades compared to the
default conditions. This suggests that while current LMs may possess abstract
task-solving skills to a degree, they often also rely on narrow,
non-transferable procedures for task-solving. These results motivate a more
careful interpretation of language model performance that teases apart these
aspects of behavior. | http://arxiv.org/pdf/2307.02477 | Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230801 | [] |
2307.02485 | 4 | We evaluate our framework on two extended embodied multi-agent cooperation challenges: Com- municative Watch-And-Help (C-WAH) and ThreeDWorld Multi-Agent Transport (TDW-MAT). Our experimental results indicate that cooperative embodied agents built with Large Language Models can plan, communicate, and cooperate with other embodied agents and humans to accomplish long- horizon tasks efï¬ciently. For example, as illustrated in Figure 1, the LLM-based agent can reason about the current state and the other agentâs state and divides the labor with its partner through com- munication effectively. In particular, by harnessing the rich world knowledge and strong reasoning capability of recent Large Language Models, such as GPT-4, our method can outperform strong planning-based baselines and exhibit emergent efï¬cient communication. In a user study, we also discover that LLM-based agents that communicate with humans in natural language can earn more trust from humans. In sum, our contribution includes:
⢠We conducted the ï¬rst systematic study on LLMsâ capacity for planning and communication in embodied multi-agent cooperation.
⢠We introduced a novel framework that utilizes LLMs to build cooperative embodied agents, surpass- ing strong planning-based methods.
⢠We conducted a user study to evaluate the possibility of achieving effective and trustworthy human- AI cooperation using LLMs.
# 2 Related Work | 2307.02485#4 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | [
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2307.02486 | 4 | The major challenge of scaling up sequence length is striking the right balance between the computational complexity and the model expressivity. RNN-style models are primarily imple- mented to increase the length. However, its sequential nature limits the parallelization dur- ing training, which is essential in long-sequence modeling. More recently, state space mod- els [GGR22, SWL23, FDS It can operate as a CNN during training, and transform to an efï¬cient RNN at test time. While they perform well at long-range benchmarks [TDA 21], their performance on regular lengths is not as good as Transformers, limited mainly by the model expressivity [FPB | 2307.02486#4 | LongNet: Scaling Transformers to 1,000,000,000 Tokens | Scaling sequence length has become a critical demand in the era of large
language models. However, existing methods struggle with either computational
complexity or model expressivity, rendering the maximum sequence length
restricted. To address this issue, we introduce LongNet, a Transformer variant
that can scale sequence length to more than 1 billion tokens, without
sacrificing the performance on shorter sequences. Specifically, we propose
dilated attention, which expands the attentive field exponentially as the
distance grows. LongNet has significant advantages: 1) it has a linear
computation complexity and a logarithm dependency between any two tokens in a
sequence; 2) it can be served as a distributed trainer for extremely long
sequences; 3) its dilated attention is a drop-in replacement for standard
attention, which can be seamlessly integrated with the existing
Transformer-based optimization. Experiments results demonstrate that LongNet
yields strong performance on both long-sequence modeling and general language
tasks. Our work opens up new possibilities for modeling very long sequences,
e.g., treating a whole corpus or even the entire Internet as a sequence. | http://arxiv.org/pdf/2307.02486 | Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, Furu Wei | cs.CL, cs.LG | Work in progress | null | cs.CL | 20230705 | 20230719 | [] |
2307.03692 | 4 | Ablation studies have shown that both the diver- sity and quality of the training data play a cru- cial role in model performance (Chen et al. 2023, Zhou et al. 2023). Low Training Data Instruction Tuning (LTD Tuning) suggests that task-speciï¬c models can gain 2% performance when trained on less than 0.5% of the original data. Moreover, pro- longed instruction tuning can decrease the founda- tional model knowledge (Gudibande et al. 2023) and can be seen as the out-of-distribution task for a downstream task of instruct-tuning (Kumar et al. 2022).
In this study, we want to lay the foundation for instruct models research by deï¬ning the neces- sary (but not sufï¬cient) condition for an instruct model. Letâs conduct a thought experiment. | 2307.03692#4 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the ratio of well formatted responses to partial and full sentences can be an
effective measure between those two model classes. Secondly, the metric can be
used as an early stopping criteria for instruct tuning. We compute IFS for
Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
learn to follow instructions relatively early in the training process, and the
further finetuning can result in changes in the underlying base model
semantics. As an example of semantics change we show the objectivity of model
predictions, as defined by an auxiliary metric ObjecQA. We show that in this
particular case, semantic changes are the steepest when the IFS tends to
plateau. We hope that decomposing instruct tuning into IFS and semantic factors
starts a new trend in better controllable instruct tuning and opens
possibilities for designing minimal instruct interfaces querying foundation
models. | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | [
{
"id": "2101.00027"
}
] |
2307.02046 | 5 | Due to the remarkable ability of representation learning in various fields, Deep Neural Networks (DNNs) have been widely adopted to advance recommender systems [9], [10]. DNNs demonstrate distinctive abilities in modeling user- item interactions with different architectures. For example, as particularly effective tools for sequential data, Recurrent Neural Networks (RNNs) have been adopted to capture high- order dependencies in user interaction sequences [11], [12]. Considering usersâ online behaviors (e.g., chick, purchase, so- cializing) as graph-structured data, Graph Neural Networks (GNNs) have emerged as advanced representation learning techniques to learn user and item representations [1], [6], [13]. Meanwhile, DNNs have also demonstrated advantages in encoding side information. For instance, a BERT-based method is proposed to extract and utilize textual reviews from users [14].
⢠Z. Wen and F. Wang are with Amazon. E-mail: {zhenwen, feiww}@amazon.com.
⢠X. Zhao is with City University of Hong Kong. E-mail:
[email protected]. J. Tang is with Michigan State University. E-mail: [email protected].
# e
(Corresponding authors: Wenqi Fan and Qing Li.) | 2307.02046#5 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling user-item interactions and incorporating textual side
information, DNN-based methods still face limitations, such as difficulties in
understanding users' interests and capturing textual side information,
inabilities in generalizing to various recommendation scenarios and reasoning
on their predictions, etc. Meanwhile, the emergence of Large Language Models
(LLMs), such as ChatGPT and GPT4, has revolutionized the fields of Natural
Language Processing (NLP) and Artificial Intelligence (AI), due to their
remarkable abilities in fundamental responsibilities of language understanding
and generation, as well as impressive generalization and reasoning
capabilities. As a result, recent studies have attempted to harness the power
of LLMs to enhance recommender systems. Given the rapid evolution of this
research direction in recommender systems, there is a pressing need for a
systematic overview that summarizes existing LLM-empowered recommender systems,
to provide researchers in relevant fields with an in-depth understanding.
Therefore, in this paper, we conduct a comprehensive review of LLM-empowered
recommender systems from various aspects including Pre-training, Fine-tuning,
and Prompting. More specifically, we first introduce representative methods to
harness the power of LLMs (as a feature encoder) for learning representations
of users and items. Then, we review recent techniques of LLMs for enhancing
recommender systems from three paradigms, namely pre-training, fine-tuning, and
prompting. Finally, we comprehensively discuss future directions in this
emerging field. | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | [
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2307.02477 | 5 | Ideally, we expect a general-purpose LM to be able to generalize not only to unseen instances of known tasks, but to new tasks. Humans, for exam- ple, can transfer their knowledge to new instances and also flexibly adapt to novel tasks (Singley and Anderson, 1989). To what extent does the perfor- mance of current LMs derive from their ability to deploy task-general reasoning skills, versus their ability to recognize and recall specific tasks seen frequently in pre-training?
Past work has focused on instance-level general- ization, but this is often complicated by data con- tamination issues (Dodge et al., 2021; Magar and Schwartz, 2022; i.a.). In this work, we are inter- ested in the modelsâ generalizability to new task variants, which has been less systematically studied for LMs (though see Li et al. (2022), Mishra et al. (2022), and Wang et al. (2022b)). | 2307.02477#5 | Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks | The impressive performance of recent language models across a wide range of
tasks suggests that they possess a degree of abstract reasoning skills. Are
these skills general and transferable, or specialized to specific tasks seen
during pretraining? To disentangle these effects, we propose an evaluation
framework based on "counterfactual" task variants that deviate from the default
assumptions underlying standard tasks. Across a suite of 11 tasks, we observe
nontrivial performance on the counterfactual variants, but nevertheless find
that performance substantially and consistently degrades compared to the
default conditions. This suggests that while current LMs may possess abstract
task-solving skills to a degree, they often also rely on narrow,
non-transferable procedures for task-solving. These results motivate a more
careful interpretation of language model performance that teases apart these
aspects of behavior. | http://arxiv.org/pdf/2307.02477 | Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, Yoon Kim | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230801 | [] |
2307.02485 | 5 | ⢠We conducted a user study to evaluate the possibility of achieving effective and trustworthy human- AI cooperation using LLMs.
# 2 Related Work
Multi-Agent Cooperation and Communication Plenty works have explored various aspects of multi-agent cooperation and communication. Some works provide various platforms for multi-agent tasks [27, 38, 43, 17, 39, 45, 2, 3]. Other works focused on methods that improves communication efï¬ciency [21, 8, 46], cooperation in visually rich domains [18], or grounding communications in environments [33]. For embodied intelligence, [35] and [36] explored the social perception of the agents during their cooperation. These works usually disable communication [17, 39, 7, 35, 36], use continuous vectors [21, 8] for communication, or use discrete symbols [27, 20, 18, 33, 38] for communication. In contrast, our work stands apart by employing large language models for communication, introducing a novel perspective that utilizes natural language to enhance multi-agent cooperation and communication. | 2307.02485#5 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we present a novel framework that utilizes LLMs for multi-agent cooperation and
tests it in various embodied environments. Our framework enables embodied
agents to plan, communicate, and cooperate with other embodied agents or humans
to accomplish long-horizon tasks efficiently. We demonstrate that recent LLMs,
such as GPT-4, can surpass strong planning-based methods and exhibit emergent
effective communication using our framework without requiring fine-tuning or
few-shot prompting. We also discover that LLM-based agents that communicate in
natural language can earn more trust and cooperate more effectively with
humans. Our research underscores the potential of LLMs for embodied AI and lays
the foundation for future research in multi-agent cooperation. Videos can be
found on the project website https://vis-www.cs.umass.edu/Co-LLM-Agents/. | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | [
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] |
2307.02486 | 5 | Another strand of scaling the sequence length is to decrease the complexity of Transformers, i.e., the quadratic complexity of self-attention. Implementing sliding windows or convolution modules over the attention is a straightforward way to make the complexity nearly linear. Nevertheless, this sacriï¬ces the ability to recall the early tokens, forgetting the prompts at the very beginning of the sequence. Sparse attention reduces the computation by sparsifying the attention matrix, preserving the possibility of recalling long-distant information. For example, [CGRS19] obtains O(N N d) time complexity with a ï¬xed sparse pattern. Besides the heuristic patterns [ZGD 20, BPC20], the learnable 23]. There are also some other efï¬- patterns prove to be useful for sparse attention [KKL20, ALdJ + 20], kernel-based cient Transformer-based variants, including low-rank attention [WLK + + 21, MKW methods [KVPF20, CLD 21], + + recurrent models [DYY 23]. Yet, none has been scaled to 1 billion tokens (see Figure 1).
Method Recurrent Vanilla Attention Sparse Attention Computation Complexity O(N d2 ) O(N 2d) â N d) O(N Dilated Attention (This Work) O(N d) | 2307.02486#5 | LongNet: Scaling Transformers to 1,000,000,000 Tokens | Scaling sequence length has become a critical demand in the era of large
language models. However, existing methods struggle with either computational
complexity or model expressivity, rendering the maximum sequence length
restricted. To address this issue, we introduce LongNet, a Transformer variant
that can scale sequence length to more than 1 billion tokens, without
sacrificing the performance on shorter sequences. Specifically, we propose
dilated attention, which expands the attentive field exponentially as the
distance grows. LongNet has significant advantages: 1) it has a linear
computation complexity and a logarithm dependency between any two tokens in a
sequence; 2) it can be served as a distributed trainer for extremely long
sequences; 3) its dilated attention is a drop-in replacement for standard
attention, which can be seamlessly integrated with the existing
Transformer-based optimization. Experiments results demonstrate that LongNet
yields strong performance on both long-sequence modeling and general language
tasks. Our work opens up new possibilities for modeling very long sequences,
e.g., treating a whole corpus or even the entire Internet as a sequence. | http://arxiv.org/pdf/2307.02486 | Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan Huang, Wenhui Wang, Nanning Zheng, Furu Wei | cs.CL, cs.LG | Work in progress | null | cs.CL | 20230705 | 20230719 | [] |
2307.03692 | 5 | Letâs put all models behind a closed API (a recent equivalent of a black box). Is the model instruct- tuned or not? Knowledge benchmarks could be similar for vanilla and instruct models for LTD tuning. Skills tests would highly depend on the model size, which is not known. The simplest way of solving the riddle would be to . . . chat with the model and judge the tone of the response. For a vanilla model, we expect a next prediction word attempt, whereas for instruct models, we ex- pect them to follow instructions. We introduce a metric that captures this tone difference - Instruct Following Score (IFS). We call this problem a "tone alignment" issue.
The IFS is deï¬ned as a ratio of "answer-like" responses to "continuation-like" responses on a predeï¬ned set of instructions, where class of a response is determined by a binary classiï¬er.
We benchmark publicly available base and in- struct models, and show that the ratio of well formatted responses to partial and full sentences can be an effective measure between vanilla and instruct following models. Moreover, we calcu- late IFS for SFT for 7B and 13B LLaMA models, in the hope of ï¬nding a stopping criterion for a minimal instruct tuning. | 2307.03692#5 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the ratio of well formatted responses to partial and full sentences can be an
effective measure between those two model classes. Secondly, the metric can be
used as an early stopping criteria for instruct tuning. We compute IFS for
Supervised Fine-Tuning (SFT) of 7B and 13B LLaMA models, showing that models
learn to follow instructions relatively early in the training process, and the
further finetuning can result in changes in the underlying base model
semantics. As an example of semantics change we show the objectivity of model
predictions, as defined by an auxiliary metric ObjecQA. We show that in this
particular case, semantic changes are the steepest when the IFS tends to
plateau. We hope that decomposing instruct tuning into IFS and semantic factors
starts a new trend in better controllable instruct tuning and opens
possibilities for designing minimal instruct interfaces querying foundation
models. | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | [
{
"id": "2101.00027"
}
] |
2307.02053 | 6 | To this end, we first sample a 1M-sized instruction dataset from the 15M-sized FLAN Collection dataset [Longpre et al., 2023] and combined it with several other datasets comprising coding tasks and ChatGPT/GPT-4 distilled conversations. The resulting smaller dataset, FLAN-MINI, is then cast into the conversational format of VICUNA. To ensure a reasonable computational cost for the fine-tuning process, we retrofit LoRA [Hu et al., 2021] adapter into the LLaMA [Touvron et al., 2023] decoder-transformer of VICUNA. Following a parameter-efficient LoRA fine-tuning of the VICUNA checkpoint on FLAN-MINI, we obtain FLACUNA. As expected, FLACUNA outperforms VICUNA by a substantial margin on most benchmark datasets, especially for reasoning-intensive tasks. However, the performance of FLACUNA still remains below FLAN-T5 on the same reasoning benchmarks. This could be attributed to the 15-times smaller dataset of the instruction dataset which may contain less diverse samples. Furthermore, full fine-tuning of VICUNA may narrow the gap with FLAN-T5.
This work overall has the following contributions: | 2307.02053#6 | Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning | Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLMs, such as LLAMA and VICUNA, on tasks that require general
problem-solving skills. This performance discrepancy can be attributed to three
key factors: (1) Pre-training data, (2) Backbone architecture, and (3)
Instruction dataset. In this technical report, our main focus is on
investigating the impact of the third factor by leveraging VICUNA, a large
language model based on LLAMA, which has undergone fine-tuning on ChatGPT
conversations. To achieve this objective, we fine-tuned VICUNA using a
customized instruction dataset collection called FLANMINI. This collection
includes a subset of the large-scale instruction dataset known as FLAN, as well
as various code-related datasets and conversational datasets derived from
ChatGPT/GPT-4. This dataset comprises a large number of tasks that demand
problem-solving skills. Our experimental findings strongly indicate that the
enhanced problem-solving abilities of our model, FLACUNA, are obtained through
fine-tuning VICUNA on the FLAN dataset, leading to significant improvements
across numerous benchmark datasets in INSTRUCTEVAL. FLACUNA is publicly
available at https://huggingface.co/declare-lab/flacuna-13b-v1.0. | http://arxiv.org/pdf/2307.02053 | Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria | cs.CL | null | null | cs.CL | 20230705 | 20230705 | [
{
"id": "2301.13688"
},
{
"id": "2106.09685"
},
{
"id": "2203.07814"
},
{
"id": "1909.09436"
}
] |
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