id
stringlengths 12
15
| title
stringlengths 8
162
| content
stringlengths 1
17.6k
| prechunk_id
stringlengths 0
15
| postchunk_id
stringlengths 0
15
| arxiv_id
stringlengths 10
10
| references
listlengths 1
1
|
---|---|---|---|---|---|---|
2309.07864#218 | The Rise and Potential of Large Language Model Based Agents: A Survey | Images speak in images: A generalist painter for in-context visual learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 6830â 6839. IEEE, 2023. [194] Wang, C., S. Chen, Y. Wu, et al. Neural codec language models are zero-shot text to speech synthesizers. CoRR, abs/2301.02111, 2023. [195] Dong, Q., L. Li, D. Dai, et al. A survey for in-context learning. CoRR, abs/2301.00234, 2023. [196] Ke, Z., B. Liu. | 2309.07864#217 | 2309.07864#219 | 2309.07864 | [
"2305.08982"
] |
2309.07864#219 | The Rise and Potential of Large Language Model Based Agents: A Survey | Continual learning of natural language processing tasks: A survey. ArXiv, abs/2211.12701, 2022. [197] Wang, L., X. Zhang, H. Su, et al. A comprehensive survey of continual learning: Theory, method and application. ArXiv, abs/2302.00487, 2023. [198] Razdaibiedina, A., Y. Mao, R. Hou, et al. Progressive prompts: Continual learning for language models. In The Eleventh International Conference on Learning Representations. 2023. [199] Marshall, L. H., H. W. Magoun. | 2309.07864#218 | 2309.07864#220 | 2309.07864 | [
"2305.08982"
] |
2309.07864#220 | The Rise and Potential of Large Language Model Based Agents: A Survey | Discoveries in the human brain: neuroscience prehistory, brain structure, and function. Springer Science & Business Media, 2013. [200] Searle, J. R. What is language: some preliminary remarks. Explorations in Pragmatics. Linguistic, cognitive and intercultural aspects, pages 7â 37, 2007. [201] Touvron, H., T. Lavril, G. Izacard, et al. | 2309.07864#219 | 2309.07864#221 | 2309.07864 | [
"2305.08982"
] |
2309.07864#221 | The Rise and Potential of Large Language Model Based Agents: A Survey | Llama: Open and efficient foundation language models. CoRR, abs/2302.13971, 2023. [202] Scao, T. L., A. Fan, C. Akiki, et al. BLOOM: A 176b-parameter open-access multilingual language model. CoRR, abs/2211.05100, 2022. [203] Almazrouei, E., H. Alobeidli, A. Alshamsi, et al. | 2309.07864#220 | 2309.07864#222 | 2309.07864 | [
"2305.08982"
] |
2309.07864#222 | The Rise and Potential of Large Language Model Based Agents: A Survey | Falcon-40b: an open large language model with state-of-the-art performance, 2023. [204] Serban, I. V., R. Lowe, L. Charlin, et al. Generative deep neural networks for dialogue: A short review. CoRR, abs/1611.06216, 2016. [205] Vinyals, O., Q. V. Le. A neural conversational model. CoRR, abs/1506.05869, 2015. | 2309.07864#221 | 2309.07864#223 | 2309.07864 | [
"2305.08982"
] |
2309.07864#223 | The Rise and Potential of Large Language Model Based Agents: A Survey | 59 [206] Adiwardana, D., M. Luong, D. R. So, et al. Towards a human-like open-domain chatbot. CoRR, abs/2001.09977, 2020. [207] Zhuge, M., H. Liu, F. Faccio, et al. Mindstorms in natural language-based societies of mind. CoRR, abs/2305.17066, 2023. [208] Roller, S., E. Dinan, N. Goyal, et al. | 2309.07864#222 | 2309.07864#224 | 2309.07864 | [
"2305.08982"
] |
2309.07864#224 | The Rise and Potential of Large Language Model Based Agents: A Survey | Recipes for building an open-domain chatbot. In P. Merlo, J. Tiedemann, R. Tsarfaty, eds., Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, April 19 - 23, 2021, pages 300â 325. Association for Computational Linguistics, 2021. [209] Taori, R., I. Gulrajani, T. Zhang, et al. Stanford alpaca: An instruction-following llama model, 2023. [210] Raffel, C., N. Shazeer, A. Roberts, et al. | 2309.07864#223 | 2309.07864#225 | 2309.07864 | [
"2305.08982"
] |
2309.07864#225 | The Rise and Potential of Large Language Model Based Agents: A Survey | Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485â 5551, 2020. [211] Ge, Y., W. Hua, J. Ji, et al. Openagi: When LLM meets domain experts. CoRR, abs/2304.04370, 2023. [212] Rajpurkar, P., J. Zhang, K. Lopyrev, et al. Squad: 100, 000+ questions for machine com- prehension of text. In J. Su, X. Carreras, K. Duh, eds., Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016, Austin, Texas, USA, November 1-4, 2016, pages 2383â 2392. The Association for Computational Linguistics, 2016. [213] Ahuja, K., R. Hada, M. Ochieng, et al. | 2309.07864#224 | 2309.07864#226 | 2309.07864 | [
"2305.08982"
] |
2309.07864#226 | The Rise and Potential of Large Language Model Based Agents: A Survey | MEGA: multilingual evaluation of generative AI. CoRR, abs/2303.12528, 2023. [214] See, A., A. Pappu, R. Saxena, et al. Do massively pretrained language models make better storytellers? In M. Bansal, A. Villavicencio, eds., Proceedings of the 23rd Conference on Computational Natural Language Learning, CoNLL 2019, Hong Kong, China, November 3-4, 2019, pages 843â 861. Association for Computational Linguistics, 2019. [215] Radford, A., J. Wu, D. Amodei, et al. Better language models and their implications. OpenAI blog, 1(2), 2019. [216] McCoy, R. T., P. Smolensky, T. Linzen, et al. How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN. CoRR, abs/2111.09509, 2021. [217] Tellex, S., T. Kollar, S. Dickerson, et al. | 2309.07864#225 | 2309.07864#227 | 2309.07864 | [
"2305.08982"
] |
2309.07864#227 | The Rise and Potential of Large Language Model Based Agents: A Survey | Understanding natural language commands for robotic navigation and mobile manipulation. In W. Burgard, D. Roth, eds., Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 7-11, 2011, pages 1507â 1514. AAAI Press, 2011. [218] Christiano, P. F., J. Leike, T. B. Brown, et al. Deep reinforcement learning from human preferences. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, R. Garnett, eds., Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 4299â 4307. 2017. [219] Basu, C., M. Singhal, A. D. | 2309.07864#226 | 2309.07864#228 | 2309.07864 | [
"2305.08982"
] |
2309.07864#228 | The Rise and Potential of Large Language Model Based Agents: A Survey | Dragan. Learning from richer human guidance: Augmenting comparison-based learning with feature queries. In T. Kanda, S. Sabanovic, G. Hoffman, A. Tapus, eds., Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2018, Chicago, IL, USA, March 05-08, 2018, pages 132â 140. ACM, 2018. [220] Sumers, T. R., M. K. Ho, R. X. D. Hawkins, et al. | 2309.07864#227 | 2309.07864#229 | 2309.07864 | [
"2305.08982"
] |
2309.07864#229 | The Rise and Potential of Large Language Model Based Agents: A Survey | Learning rewards from linguistic feedback. In Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pages 6002â 6010. AAAI Press, 2021. 60 [221] Jeon, H. J., S. Milli, A. D. Dragan. Reward-rational (implicit) choice: A unifying formalism for reward learning. | 2309.07864#228 | 2309.07864#230 | 2309.07864 | [
"2305.08982"
] |
2309.07864#230 | The Rise and Potential of Large Language Model Based Agents: A Survey | In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin, eds., Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020. [222] McShane, M. Reference resolution challenges for intelligent agents: The need for knowledge. IEEE Intell. Syst., 24(4):47â 58, 2009. [223] Gururangan, S., A. Marasovic, S. Swayamdipta, et al. Donâ | 2309.07864#229 | 2309.07864#231 | 2309.07864 | [
"2305.08982"
] |
2309.07864#231 | The Rise and Potential of Large Language Model Based Agents: A Survey | t stop pretraining: Adapt language In D. Jurafsky, J. Chai, N. Schluter, J. R. Tetreault, eds., models to domains and tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 8342â 8360. Association for Computational Linguistics, 2020. [224] Shi, F., X. Chen, K. Misra, et al. | 2309.07864#230 | 2309.07864#232 | 2309.07864 | [
"2305.08982"
] |
2309.07864#232 | The Rise and Potential of Large Language Model Based Agents: A Survey | Large language models can be easily distracted by irrelevant context. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. Scarlett, eds., International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol. 202 of Proceedings of Machine Learning Research, pages 31210â 31227. PMLR, 2023. [225] Zhang, Y., Y. Li, L. Cui, et al. | 2309.07864#231 | 2309.07864#233 | 2309.07864 | [
"2305.08982"
] |
2309.07864#233 | The Rise and Potential of Large Language Model Based Agents: A Survey | Sirenâ s song in the AI ocean: A survey on hallucination in large language models. CoRR, abs/2309.01219, 2023. [226] Mialon, G., R. Dessì, M. Lomeli, et al. Augmented language models: a survey. CoRR, abs/2302.07842, 2023. [227] Ren, R., Y. Wang, Y. Qu, et al. Investigating the factual knowledge boundary of large language models with retrieval augmentation. CoRR, abs/2307.11019, 2023. [228] Nuxoll, A. M., J. E. Laird. | 2309.07864#232 | 2309.07864#234 | 2309.07864 | [
"2305.08982"
] |
2309.07864#234 | The Rise and Potential of Large Language Model Based Agents: A Survey | Extending cognitive architecture with episodic memory. In AAAI, pages 1560â 1564. 2007. [229] Squire, L. R. Mechanisms of memory. Science, 232(4758):1612â 1619, 1986. [230] Schwabe, L., K. Nader, J. C. Pruessner. Reconsolidation of human memory: brain mechanisms and clinical relevance. Biological psychiatry, 76(4):274â 280, 2014. [231] Hutter, M. | 2309.07864#233 | 2309.07864#235 | 2309.07864 | [
"2305.08982"
] |
2309.07864#235 | The Rise and Potential of Large Language Model Based Agents: A Survey | A theory of universal artificial intelligence based on algorithmic complexity. arXiv preprint cs/0004001, 2000. [232] Zhang, X., F. Wei, M. Zhou. HIBERT: document level pre-training of hierarchical bidirectional transformers for document summarization. In A. Korhonen, D. R. Traum, L. MÃ rquez, eds., Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, Volume 1: Long Papers, pages 5059â 5069. Association for Computational Linguistics, 2019. [233] Mohtashami, A., M. | 2309.07864#234 | 2309.07864#236 | 2309.07864 | [
"2305.08982"
] |
2309.07864#236 | The Rise and Potential of Large Language Model Based Agents: A Survey | Jaggi. Landmark attention: Random-access infinite context length for transformers. CoRR, abs/2305.16300, 2023. [234] Chalkidis, I., X. Dai, M. Fergadiotis, et al. An exploration of hierarchical attention transformers for efficient long document classification. CoRR, abs/2210.05529, 2022. [235] Nie, Y., H. Huang, W. Wei, et al. Capturing global structural information in long document question answering with compressive graph selector network. In Y. Goldberg, Z. Kozareva, Y. Zhang, eds., Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 5036â 5047. Association for Computational Linguistics, 2022. [236] Bertsch, A., U. Alon, G. Neubig, et al. Unlimiformer: Long-range transformers with unlimited length input. CoRR, abs/2305.01625, 2023. | 2309.07864#235 | 2309.07864#237 | 2309.07864 | [
"2305.08982"
] |
2309.07864#237 | The Rise and Potential of Large Language Model Based Agents: A Survey | 61 [237] Manakul, P., M. J. F. Gales. Sparsity and sentence structure in encoder-decoder attention of summarization systems. In M. Moens, X. Huang, L. Specia, S. W. Yih, eds., Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021, pages 9359â 9368. Association for Computational Linguistics, 2021. [238] Zaheer, M., G. Guruganesh, K. A. Dubey, et al. Big bird: | 2309.07864#236 | 2309.07864#238 | 2309.07864 | [
"2305.08982"
] |
2309.07864#238 | The Rise and Potential of Large Language Model Based Agents: A Survey | Transformers for longer sequences. In H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, H. Lin, eds., Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020. [239] Zhao, A., D. Huang, Q. Xu, et al. | 2309.07864#237 | 2309.07864#239 | 2309.07864 | [
"2305.08982"
] |
2309.07864#239 | The Rise and Potential of Large Language Model Based Agents: A Survey | Expel: LLM agents are experiential learners. CoRR, abs/2308.10144, 2023. [240] Zhou, X., G. Li, Z. Liu. LLM as DBA. CoRR, abs/2308.05481, 2023. [241] Wason, P. C. Reasoning about a rule. Quarterly journal of experimental psychology, 20(3):273â 281, 1968. [242] Wason, P. C., P. N. Johnson-Laird. Psychology of reasoning: | 2309.07864#238 | 2309.07864#240 | 2309.07864 | [
"2305.08982"
] |
2309.07864#240 | The Rise and Potential of Large Language Model Based Agents: A Survey | Structure and content, vol. 86. Harvard University Press, 1972. [243] Galotti, K. M. Approaches to studying formal and everyday reasoning. Psychological bulletin, 105(3):331, 1989. [244] Huang, J., K. C. Chang. Towards reasoning in large language models: A survey. In A. Rogers, J. L. Boyd-Graber, N. Okazaki, eds., Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pages 1049â 1065. Association for Computational Linguistics, 2023. [245] Webb, T. W., K. J. Holyoak, H. Lu. | 2309.07864#239 | 2309.07864#241 | 2309.07864 | [
"2305.08982"
] |
2309.07864#241 | The Rise and Potential of Large Language Model Based Agents: A Survey | Emergent analogical reasoning in large language models. CoRR, abs/2212.09196, 2022. [246] Feng, G., B. Zhang, Y. Gu, et al. Towards revealing the mystery behind chain of thought: a theoretical perspective. CoRR, abs/2305.15408, 2023. [247] Grafman, J., L. Spector, M. J. Rattermann. Planning and the brain. In The cognitive psychology of planning, pages 191â 208. Psychology Press, 2004. [248] Unterrainer, J. M., A. M. Owen. | 2309.07864#240 | 2309.07864#242 | 2309.07864 | [
"2305.08982"
] |
2309.07864#242 | The Rise and Potential of Large Language Model Based Agents: A Survey | Planning and problem solving: from neuropsychology to functional neuroimaging. Journal of Physiology-Paris, 99(4-6):308â 317, 2006. [249] Zula, K. J., T. J. Chermack. Integrative literature review: Human capital planning: A review of literature and implications for human resource development. Human Resource Development Review, 6(3):245â 262, 2007. [250] Bratman, M. E., D. J. Israel, M. E. Pollack. | 2309.07864#241 | 2309.07864#243 | 2309.07864 | [
"2305.08982"
] |
2309.07864#243 | The Rise and Potential of Large Language Model Based Agents: A Survey | Plans and resource-bounded practical reasoning. Computational intelligence, 4(3):349â 355, 1988. [251] Russell, S., P. Norvig. Artificial intelligence - a modern approach, 2nd Edition. Prentice Hall series in artificial intelligence. Prentice Hall, 2003. [252] Fainstein, S. S., J. DeFilippis. Readings in planning theory. John Wiley & Sons, 2015. [253] Sebastia, L., E. Onaindia, E. Marzal. | 2309.07864#242 | 2309.07864#244 | 2309.07864 | [
"2305.08982"
] |
2309.07864#244 | The Rise and Potential of Large Language Model Based Agents: A Survey | Decomposition of planning problems. Ai Communica- tions, 19(1):49â 81, 2006. [254] Crosby, M., M. Rovatsos, R. Petrick. Automated agent decomposition for classical planning. In Proceedings of the International Conference on Automated Planning and Scheduling, vol. 23, pages 46â 54. 2013. [255] Xu, B., Z. Peng, B. Lei, et al. Rewoo: Decoupling reasoning from observations for efficient augmented language models. CoRR, abs/2305.18323, 2023. | 2309.07864#243 | 2309.07864#245 | 2309.07864 | [
"2305.08982"
] |
2309.07864#245 | The Rise and Potential of Large Language Model Based Agents: A Survey | 62 [256] Raman, S. S., V. Cohen, E. Rosen, et al. Planning with large language models via corrective re-prompting. CoRR, abs/2211.09935, 2022. [257] Lyu, Q., S. Havaldar, A. Stein, et al. Faithful chain-of-thought reasoning. CoRR, abs/2301.13379, 2023. [258] Huang, W., P. Abbeel, D. Pathak, et al. | 2309.07864#244 | 2309.07864#246 | 2309.07864 | [
"2305.08982"
] |
2309.07864#246 | The Rise and Potential of Large Language Model Based Agents: A Survey | Language models as zero-shot planners: Extracting ac- tionable knowledge for embodied agents. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, S. Sabato, eds., International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, vol. 162 of Proceedings of Machine Learning Research, pages 9118â 9147. PMLR, 2022. [259] Dagan, G., F. Keller, A. Lascarides. Dynamic planning with a LLM. CoRR, abs/2308.06391, 2023. [260] Rana, K., J. Haviland, S. Garg, et al. Sayplan: Grounding large language models using 3d scene graphs for scalable task planning. CoRR, abs/2307.06135, 2023. [261] Peters, M. E., M. Neumann, M. Iyyer, et al. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 2227â 2237. Association for Computational Linguistics, New Orleans, Louisiana, 2018. [262] Devlin, J., M. Chang, K. Lee, et al. | 2309.07864#245 | 2309.07864#247 | 2309.07864 | [
"2305.08982"
] |
2309.07864#247 | The Rise and Potential of Large Language Model Based Agents: A Survey | BERT: pre-training of deep bidirectional transformers for language understanding. In J. Burstein, C. Doran, T. Solorio, eds., Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguis- tics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pages 4171â 4186. Association for Computational Linguistics, 2019. [263] Solaiman, I., C. Dennison. | 2309.07864#246 | 2309.07864#248 | 2309.07864 | [
"2305.08982"
] |
2309.07864#248 | The Rise and Potential of Large Language Model Based Agents: A Survey | Process for adapting language models to society (palms) with values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861â 5873, 2021. [264] Bach, S. H., V. Sanh, Z. X. Yong, et al. Promptsource: An integrated development environment and repository for natural language prompts. In V. Basile, Z. Kozareva, S. Stajner, eds., Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022 - System Demonstrations, Dublin, Ireland, May 22-27, 2022, pages 93â | 2309.07864#247 | 2309.07864#249 | 2309.07864 | [
"2305.08982"
] |
2309.07864#249 | The Rise and Potential of Large Language Model Based Agents: A Survey | 104. Association for Computational Linguistics, 2022. [265] Iyer, S., X. V. Lin, R. Pasunuru, et al. OPT-IML: scaling language model instruction meta learning through the lens of generalization. CoRR, abs/2212.12017, 2022. [266] Winston, P. H. Learning and reasoning by analogy. Commun. ACM, 23(12):689â 703, 1980. [267] Lu, Y., M. Bartolo, A. Moore, et al. | 2309.07864#248 | 2309.07864#250 | 2309.07864 | [
"2305.08982"
] |
2309.07864#250 | The Rise and Potential of Large Language Model Based Agents: A Survey | Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity. In S. Muresan, P. Nakov, A. Villavicencio, eds., Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, pages 8086â 8098. Association for Computational Linguistics, 2022. [268] Tsimpoukelli, M., J. Menick, S. Cabi, et al. | 2309.07864#249 | 2309.07864#251 | 2309.07864 | [
"2305.08982"
] |
2309.07864#251 | The Rise and Potential of Large Language Model Based Agents: A Survey | Multimodal few-shot learning with frozen language models. In M. Ranzato, A. Beygelzimer, Y. N. Dauphin, P. Liang, J. W. Vaughan, eds., Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 200â 212. 2021. [269] Bar, A., Y. Gandelsman, T. Darrell, et al. | 2309.07864#250 | 2309.07864#252 | 2309.07864 | [
"2305.08982"
] |
2309.07864#252 | The Rise and Potential of Large Language Model Based Agents: A Survey | Visual prompting via image inpainting. In NeurIPS. 2022. [270] Zhu, W., H. Liu, Q. Dong, et al. Multilingual machine translation with large language models: Empirical results and analysis. CoRR, abs/2304.04675, 2023. 63 [271] Zhang, Z., L. Zhou, C. Wang, et al. Speak foreign languages with your own voice: Cross- lingual neural codec language modeling. CoRR, abs/2303.03926, 2023. [272] Zhang, J., J. Zhang, K. Pertsch, et al. | 2309.07864#251 | 2309.07864#253 | 2309.07864 | [
"2305.08982"
] |
2309.07864#253 | The Rise and Potential of Large Language Model Based Agents: A Survey | Bootstrap your own skills: Learning to solve new tasks with large language model guidance. In 7th Annual Conference on Robot Learning. 2023. [273] McCloskey, M., N. J. Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation, 24:109â 165, 1989. [274] Kirkpatrick, J., R. Pascanu, N. Rabinowitz, et al. | 2309.07864#252 | 2309.07864#254 | 2309.07864 | [
"2305.08982"
] |
2309.07864#254 | The Rise and Potential of Large Language Model Based Agents: A Survey | Overcoming catastrophic forgetting in neural networks. Proceedings of the national academy of sciences, 114(13):3521â 3526, 2017. [275] Li, Z., D. Hoiem. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence, 40(12):2935â 2947, 2017. [276] Farajtabar, M., N. Azizan, A. Mott, et al. Orthogonal gradient descent for continual learning. | 2309.07864#253 | 2309.07864#255 | 2309.07864 | [
"2305.08982"
] |
2309.07864#255 | The Rise and Potential of Large Language Model Based Agents: A Survey | In International Conference on Artificial Intelligence and Statistics, pages 3762â 3773. PMLR, 2020. [277] Smith, J. S., Y.-C. Hsu, L. Zhang, et al. Continual diffusion: Continual customization of text-to-image diffusion with c-lora. arXiv preprint arXiv:2304.06027, 2023. [278] Lopez-Paz, D., M. Ranzato. | 2309.07864#254 | 2309.07864#256 | 2309.07864 | [
"2305.08982"
] |
2309.07864#256 | The Rise and Potential of Large Language Model Based Agents: A Survey | Gradient episodic memory for continual learning. Advances in neural information processing systems, 30, 2017. [279] de Masson Dâ Autume, C., S. Ruder, L. Kong, et al. Episodic memory in lifelong language learning. Advances in Neural Information Processing Systems, 32, 2019. [280] Rolnick, D., A. Ahuja, J. Schwarz, et al. Experience replay for continual learning. Advances in Neural Information Processing Systems, 32, 2019. | 2309.07864#255 | 2309.07864#257 | 2309.07864 | [
"2305.08982"
] |
2309.07864#257 | The Rise and Potential of Large Language Model Based Agents: A Survey | [281] Serrà , J., D. Surà s, M. Miron, et al. Overcoming catastrophic forgetting with hard attention to the task. In International Conference on Machine Learning. 2018. [282] Dosovitskiy, A., L. Beyer, A. Kolesnikov, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. [283] van den Oord, A., O. Vinyals, K. Kavukcuoglu. | 2309.07864#256 | 2309.07864#258 | 2309.07864 | [
"2305.08982"
] |
2309.07864#258 | The Rise and Potential of Large Language Model Based Agents: A Survey | Neural discrete representation learning. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, R. Garnett, eds., Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 6306â 6315. 2017. [284] Mehta, S., M. Rastegari. Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. | 2309.07864#257 | 2309.07864#259 | 2309.07864 | [
"2305.08982"
] |
2309.07864#259 | The Rise and Potential of Large Language Model Based Agents: A Survey | In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022. [285] Tolstikhin, I. O., N. Houlsby, A. Kolesnikov, et al. Mlp-mixer: An all-mlp architecture for vision. In M. Ranzato, A. Beygelzimer, Y. N. Dauphin, P. Liang, J. W. Vaughan, eds., Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 24261â 24272. 2021. [286] Huang, S., L. Dong, W. Wang, et al. | 2309.07864#258 | 2309.07864#260 | 2309.07864 | [
"2305.08982"
] |
2309.07864#260 | The Rise and Potential of Large Language Model Based Agents: A Survey | Language is not all you need: Aligning perception with language models. CoRR, abs/2302.14045, 2023. [287] Li, J., D. Li, S. Savarese, et al. BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. | 2309.07864#259 | 2309.07864#261 | 2309.07864 | [
"2305.08982"
] |
2309.07864#261 | The Rise and Potential of Large Language Model Based Agents: A Survey | Scarlett, eds., International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol. 202 of Proceedings of Machine Learning Research, pages 19730â 19742. PMLR, 2023. [288] Dai, W., J. Li, D. Li, et al. Instructblip: Towards general-purpose vision-language models with instruction tuning. CoRR, abs/2305.06500, 2023. | 2309.07864#260 | 2309.07864#262 | 2309.07864 | [
"2305.08982"
] |
2309.07864#262 | The Rise and Potential of Large Language Model Based Agents: A Survey | 64 [289] Gong, T., C. Lyu, S. Zhang, et al. Multimodal-gpt: A vision and language model for dialogue with humans. CoRR, abs/2305.04790, 2023. [290] Alayrac, J., J. Donahue, P. Luc, et al. Flamingo: a visual language model for few-shot learning. In NeurIPS. 2022. [291] Su, Y., T. Lan, H. Li, et al. Pandagpt: One model to instruction-follow them all. CoRR, abs/2305.16355, 2023. [292] Liu, H., C. Li, Q. Wu, et al. | 2309.07864#261 | 2309.07864#263 | 2309.07864 | [
"2305.08982"
] |
2309.07864#263 | The Rise and Potential of Large Language Model Based Agents: A Survey | Visual instruction tuning. CoRR, abs/2304.08485, 2023. [293] Huang, R., M. Li, D. Yang, et al. Audiogpt: Understanding and generating speech, music, sound, and talking head. CoRR, abs/2304.12995, 2023. [294] Gong, Y., Y. Chung, J. R. Glass. AST: audio spectrogram transformer. In H. Hermansky, H. Cernocký, L. Burget, L. Lamel, O. Scharenborg, P. Motlà cek, eds., Interspeech 2021, 22nd Annual Conference of the International Speech Communication Association, Brno, Czechia, 30 August - 3 September 2021, pages 571â 575. ISCA, 2021. [295] Hsu, W., B. Bolte, Y. H. Tsai, et al. Hubert: | 2309.07864#262 | 2309.07864#264 | 2309.07864 | [
"2305.08982"
] |
2309.07864#264 | The Rise and Potential of Large Language Model Based Agents: A Survey | Self-supervised speech representation learning IEEE ACM Trans. Audio Speech Lang. Process., by masked prediction of hidden units. 29:3451â 3460, 2021. [296] Chen, F., M. Han, H. Zhao, et al. X-LLM: bootstrapping advanced large language models by treating multi-modalities as foreign languages. CoRR, abs/2305.04160, 2023. [297] Zhang, H., X. Li, L. Bing. Video-llama: An instruction-tuned audio-visual language model for video understanding. | 2309.07864#263 | 2309.07864#265 | 2309.07864 | [
"2305.08982"
] |
2309.07864#265 | The Rise and Potential of Large Language Model Based Agents: A Survey | CoRR, abs/2306.02858, 2023. [298] Liu, Z., Y. He, W. Wang, et al. Interngpt: Solving vision-centric tasks by interacting with chatbots beyond language. CoRR, abs/2305.05662, 2023. [299] Hubel, D. H., T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the catâ s visual cortex. | 2309.07864#264 | 2309.07864#266 | 2309.07864 | [
"2305.08982"
] |
2309.07864#266 | The Rise and Potential of Large Language Model Based Agents: A Survey | The Journal of physiology, 160(1):106, 1962. [300] Logothetis, N. K., D. L. Sheinberg. Visual object recognition. Annual review of neuroscience, 19(1):577â 621, 1996. [301] OpenAI. Openai: Introducing chatgpt. Website, 2022. https://openai.com/blog/ chatgpt. [302] Lu, J., X. Ren, Y. Ren, et al. | 2309.07864#265 | 2309.07864#267 | 2309.07864 | [
"2305.08982"
] |
2309.07864#267 | The Rise and Potential of Large Language Model Based Agents: A Survey | Improving contextual language models for response retrieval in multi-turn conversation. In J. X. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J. Wen, Y. Liu, eds., Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 1805â | 2309.07864#266 | 2309.07864#268 | 2309.07864 | [
"2305.08982"
] |
2309.07864#268 | The Rise and Potential of Large Language Model Based Agents: A Survey | 1808. ACM, 2020. [303] Huang, L., W. Wang, J. Chen, et al. Attention on attention for image captioning. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pages 4633â 4642. IEEE, 2019. [304] Pan, Y., T. Yao, Y. Li, et al. X-linear attention networks for image captioning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 10968â | 2309.07864#267 | 2309.07864#269 | 2309.07864 | [
"2305.08982"
] |
2309.07864#269 | The Rise and Potential of Large Language Model Based Agents: A Survey | 10977. Computer Vision Foundation / IEEE, 2020. [305] Cornia, M., M. Stefanini, L. Baraldi, et al. M2: Meshed-memory transformer for image captioning. CoRR, abs/1912.08226, 2019. [306] Chen, J., H. Guo, K. Yi, et al. Visualgpt: Data-efficient image captioning by balancing visual input and linguistic knowledge from pretraining. | 2309.07864#268 | 2309.07864#270 | 2309.07864 | [
"2305.08982"
] |
2309.07864#270 | The Rise and Potential of Large Language Model Based Agents: A Survey | CoRR, abs/2102.10407, 2021. [307] Li, K., Y. He, Y. Wang, et al. Videochat: Chat-centric video understanding. CoRR, abs/2305.06355, 2023. 65 [308] Lin, J., Y. Du, O. Watkins, et al. Learning to model the world with language. CoRR, abs/2308.01399, 2023. [309] Vaswani, A., N. Shazeer, N. Parmar, et al. | 2309.07864#269 | 2309.07864#271 | 2309.07864 | [
"2305.08982"
] |
2309.07864#271 | The Rise and Potential of Large Language Model Based Agents: A Survey | Attention is all you need. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, R. Garnett, eds., Advances in Neural Information Processing Systems 30: Annual Conference on Neural In- formation Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5998â 6008. 2017. [310] Touvron, H., M. Cord, M. Douze, et al. | 2309.07864#270 | 2309.07864#272 | 2309.07864 | [
"2305.08982"
] |
2309.07864#272 | The Rise and Potential of Large Language Model Based Agents: A Survey | Training data-efficient image transformers & distil- lation through attention. In M. Meila, T. Zhang, eds., Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, vol. 139 of Proceedings of Machine Learning Research, pages 10347â 10357. PMLR, 2021. [311] Lu, J., C. Clark, R. Zellers, et al. | 2309.07864#271 | 2309.07864#273 | 2309.07864 | [
"2305.08982"
] |
2309.07864#273 | The Rise and Potential of Large Language Model Based Agents: A Survey | UNIFIED-IO: A unified model for vision, language, and multi-modal tasks. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. [312] Peng, Z., W. Wang, L. Dong, et al. Kosmos-2: Grounding multimodal large language models to the world. CoRR, abs/2306.14824, 2023. [313] Lyu, C., M. Wu, L. Wang, et al. Macaw-llm: Multi-modal language modeling with image, audio, video, and text integration. CoRR, abs/2306.09093, 2023. [314] Maaz, M., H. A. Rasheed, S. H. Khan, et al. Video-chatgpt: Towards detailed video under- standing via large vision and language models. CoRR, abs/2306.05424, 2023. [315] Chen, M., I. Laina, A. | 2309.07864#272 | 2309.07864#274 | 2309.07864 | [
"2305.08982"
] |
2309.07864#274 | The Rise and Potential of Large Language Model Based Agents: A Survey | Vedaldi. Training-free layout control with cross-attention guidance. CoRR, abs/2304.03373, 2023. [316] Radford, A., J. W. Kim, T. Xu, et al. Robust speech recognition via large-scale weak su- pervision. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. Scarlett, eds., International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol. 202 of Proceedings of Machine Learning Research, pages 28492â 28518. PMLR, 2023. [317] Ren, Y., Y. Ruan, X. Tan, et al. | 2309.07864#273 | 2309.07864#275 | 2309.07864 | [
"2305.08982"
] |
2309.07864#275 | The Rise and Potential of Large Language Model Based Agents: A Survey | Fastspeech: Fast, robust and controllable text to speech. In H. M. Wallach, H. Larochelle, A. Beygelzimer, F. dâ Alché-Buc, E. B. Fox, R. Garnett, eds., Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 3165â 3174. 2019. [318] Ye, Z., Z. Zhao, Y. Ren, et al. | 2309.07864#274 | 2309.07864#276 | 2309.07864 | [
"2305.08982"
] |
2309.07864#276 | The Rise and Potential of Large Language Model Based Agents: A Survey | Syntaspeech: Syntax-aware generative adversarial text-to-speech. In L. D. Raedt, ed., Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, pages 4468â 4474. ijcai.org, 2022. [319] Kim, J., J. Kong, J. Son. Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In M. Meila, T. Zhang, eds., Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, vol. 139 of Proceedings of Machine Learning Research, pages 5530â 5540. PMLR, 2021. [320] Wang, Z., S. Cornell, S. Choi, et al. | 2309.07864#275 | 2309.07864#277 | 2309.07864 | [
"2305.08982"
] |
2309.07864#277 | The Rise and Potential of Large Language Model Based Agents: A Survey | Tf-gridnet: Integrating full- and sub-band modeling for speech separation. IEEE ACM Trans. Audio Speech Lang. Process., 31:3221â 3236, 2023. [321] Liu, J., C. Li, Y. Ren, et al. Diffsinger: Singing voice synthesis via shallow diffusion mechanism. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty- Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 11020â | 2309.07864#276 | 2309.07864#278 | 2309.07864 | [
"2305.08982"
] |
2309.07864#278 | The Rise and Potential of Large Language Model Based Agents: A Survey | 11028. AAAI Press, 2022. [322] Inaguma, H., S. Dalmia, B. Yan, et al. Fast-md: Fast multi-decoder end-to-end speech transla- tion with non-autoregressive hidden intermediates. In IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021, Cartagena, Colombia, December 13-17, 2021, pages 922â 929. IEEE, 2021. 66 [323] Flanagan, J. L. Speech analysis synthesis and perception, vol. 3. Springer Science & Business Media, 2013. [324] Schwarz, B. | 2309.07864#277 | 2309.07864#279 | 2309.07864 | [
"2305.08982"
] |
2309.07864#279 | The Rise and Potential of Large Language Model Based Agents: A Survey | Mapping the world in 3d. Nature Photonics, 4(7):429â 430, 2010. [325] Parkinson, B. W., J. J. Spilker. Progress in astronautics and aeronautics: Global positioning system: Theory and applications, vol. 164. Aiaa, 1996. [326] Parisi, A., Y. Zhao, N. Fiedel. TALM: tool augmented language models. CoRR, abs/2205.12255, 2022. [327] Clarebout, G., J. Elen, N. A. J. Collazo, et al. Metacognition and the Use of Tools, pages 187â 195. Springer New York, New York, NY, 2013. [328] Wu, C., S. Yin, W. Qi, et al. Visual chatgpt: Talking, drawing and editing with visual foundation models. | 2309.07864#278 | 2309.07864#280 | 2309.07864 | [
"2305.08982"
] |
2309.07864#280 | The Rise and Potential of Large Language Model Based Agents: A Survey | CoRR, abs/2303.04671, 2023. [329] Cai, T., X. Wang, T. Ma, et al. Large language models as tool makers. CoRR, abs/2305.17126, 2023. [330] Qian, C., C. Han, Y. R. Fung, et al. CREATOR: disentangling abstract and concrete reasonings of large language models through tool creation. CoRR, abs/2305.14318, 2023. [331] Chen, X., M. Lin, N. Schärli, et al. Teaching large language models to self-debug. CoRR, abs/2304.05128, 2023. [332] Liu, H., L. Lee, K. Lee, et al. Instruction-following agents with jointly pre-trained vision- language models. arXiv preprint arXiv:2210.13431, 2022. [333] Lynch, C., A. Wahid, J. Tompson, et al. | 2309.07864#279 | 2309.07864#281 | 2309.07864 | [
"2305.08982"
] |
2309.07864#281 | The Rise and Potential of Large Language Model Based Agents: A Survey | Interactive language: Talking to robots in real time. CoRR, abs/2210.06407, 2022. [334] Jin, C., W. Tan, J. Yang, et al. Alphablock: Embodied finetuning for vision-language reasoning in robot manipulation. CoRR, abs/2305.18898, 2023. [335] Shah, D., B. Osinski, B. Ichter, et al. Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action. In K. Liu, D. Kulic, J. Ichnowski, eds., Conference on Robot Learning, CoRL 2022, 14-18 December 2022, Auckland, New Zealand, vol. 205 of Proceedings of Machine Learning Research, pages 492â 504. PMLR, 2022. [336] Zhou, G., Y. Hong, Q. Wu. | 2309.07864#280 | 2309.07864#282 | 2309.07864 | [
"2305.08982"
] |
2309.07864#282 | The Rise and Potential of Large Language Model Based Agents: A Survey | Navgpt: Explicit reasoning in vision-and-language navigation with large language models. CoRR, abs/2305.16986, 2023. [337] Fan, L., G. Wang, Y. Jiang, et al. Minedojo: Building open-ended embodied agents with internet-scale knowledge. In NeurIPS. 2022. [338] Kanitscheider, I., J. Huizinga, D. Farhi, et al. Multi-task curriculum learning in a complex, visual, hard-exploration domain: | 2309.07864#281 | 2309.07864#283 | 2309.07864 | [
"2305.08982"
] |
2309.07864#283 | The Rise and Potential of Large Language Model Based Agents: A Survey | Minecraft. CoRR, abs/2106.14876, 2021. [339] Nottingham, K., P. Ammanabrolu, A. Suhr, et al. Do embodied agents dream of pixelated sheep: Embodied decision making using language guided world modelling. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. Scarlett, eds., International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol. 202 of Proceedings of Machine Learning Research, pages 26311â 26325. PMLR, 2023. [340] Sumers, T., K. Marino, A. Ahuja, et al. | 2309.07864#282 | 2309.07864#284 | 2309.07864 | [
"2305.08982"
] |
2309.07864#284 | The Rise and Potential of Large Language Model Based Agents: A Survey | Distilling internet-scale vision-language models into embodied agents. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. Scarlett, eds., International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, vol. 202 of Proceedings of Machine Learning Research, pages 32797â 32818. PMLR, 2023. [341] Carlini, N., J. Hayes, M. Nasr, et al. | 2309.07864#283 | 2309.07864#285 | 2309.07864 | [
"2305.08982"
] |
2309.07864#285 | The Rise and Potential of Large Language Model Based Agents: A Survey | Extracting training data from diffusion models. CoRR, abs/2301.13188, 2023. 67 [342] Savelka, J., K. D. Ashley, M. A. Gray, et al. Can GPT-4 support analysis of textual data in tasks requiring highly specialized domain expertise? In F. Lagioia, J. Mumford, D. Odekerken, H. Westermann, eds., Proceedings of the 6th Workshop on Automated Semantic Analysis of Information in Legal Text co-located with the 19th International Conference on Artificial Intelligence and Law (ICAIL 2023), Braga, Portugal, 23rd September, 2023, vol. 3441 of CEUR Workshop Proceedings, pages 1â 12. CEUR-WS.org, 2023. [343] Ling, C., X. Zhao, J. Lu, et al. | 2309.07864#284 | 2309.07864#286 | 2309.07864 | [
"2305.08982"
] |
2309.07864#286 | The Rise and Potential of Large Language Model Based Agents: A Survey | Domain specialization as the key to make large language models disruptive: A comprehensive survey, 2023. [344] Linardatos, P., V. Papastefanopoulos, S. Kotsiantis. Explainable AI: A review of machine learning interpretability methods. Entropy, 23(1):18, 2021. [345] Zou, A., Z. Wang, J. Z. Kolter, et al. Universal and transferable adversarial attacks on aligned language models. CoRR, abs/2307.15043, 2023. [346] Hussein, A., M. M. Gaber, E. Elyan, et al. Imitation learning: A survey of learning methods. | 2309.07864#285 | 2309.07864#287 | 2309.07864 | [
"2305.08982"
] |
2309.07864#287 | The Rise and Potential of Large Language Model Based Agents: A Survey | ACM Comput. Surv., 50(2):21:1â 21:35, 2017. [347] Liu, Y., A. Gupta, P. Abbeel, et al. Imitation from observation: Learning to imitate behaviors from raw video via context translation. In 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018, pages 1118â 1125. IEEE, 2018. [348] Baker, B., I. Akkaya, P. Zhokov, et al. | 2309.07864#286 | 2309.07864#288 | 2309.07864 | [
"2305.08982"
] |
2309.07864#288 | The Rise and Potential of Large Language Model Based Agents: A Survey | Video pretraining (VPT): learning to act by watching unlabeled online videos. In NeurIPS. 2022. [349] Levine, S., P. Pastor, A. Krizhevsky, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. Int. J. Robotics Res., 37(4-5):421â 436, 2018. [350] Zheng, R., S. Dou, S. Gao, et al. | 2309.07864#287 | 2309.07864#289 | 2309.07864 | [
"2305.08982"
] |
2309.07864#289 | The Rise and Potential of Large Language Model Based Agents: A Survey | Secrets of RLHF in large language models part I: PPO. CoRR, abs/2307.04964, 2023. [351] Bengio, Y., J. Louradour, R. Collobert, et al. Curriculum learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML â 09, page 41â 48. Association for Computing Machinery, New York, NY, USA, 2009. [352] Chen, M., J. Tworek, H. Jun, et al. | 2309.07864#288 | 2309.07864#290 | 2309.07864 | [
"2305.08982"
] |
2309.07864#290 | The Rise and Potential of Large Language Model Based Agents: A Survey | Evaluating large language models trained on code, 2021. [353] Pan, S., L. Luo, Y. Wang, et al. Unifying large language models and knowledge graphs: A roadmap. CoRR, abs/2306.08302, 2023. [354] Bran, A. M., S. Cox, A. D. White, et al. Chemcrow: Augmenting large-language models with chemistry tools, 2023. [355] Ruan, J., Y. Chen, B. Zhang, et al. | 2309.07864#289 | 2309.07864#291 | 2309.07864 | [
"2305.08982"
] |
2309.07864#291 | The Rise and Potential of Large Language Model Based Agents: A Survey | TPTU: task planning and tool usage of large language model-based AI agents. CoRR, abs/2308.03427, 2023. [356] Ogundare, O., S. Madasu, N. Wiggins. Industrial engineering with large language models: A case study of chatgptâ s performance on oil & gas problems, 2023. [357] Smith, L., M. Gasser. The development of embodied cognition: Six lessons from babies. Artificial life, 11(1-2):13â | 2309.07864#290 | 2309.07864#292 | 2309.07864 | [
"2305.08982"
] |
2309.07864#292 | The Rise and Potential of Large Language Model Based Agents: A Survey | 29, 2005. [358] Duan, J., S. Yu, H. L. Tan, et al. A survey of embodied AI: from simulators to research tasks. IEEE Trans. Emerg. Top. Comput. Intell., 6(2):230â 244, 2022. [359] Mnih, V., K. Kavukcuoglu, D. Silver, et al. Playing atari with deep reinforcement learning. CoRR, abs/1312.5602, 2013. [360] Silver, D., A. Huang, C. J. Maddison, et al. | 2309.07864#291 | 2309.07864#293 | 2309.07864 | [
"2305.08982"
] |
2309.07864#293 | The Rise and Potential of Large Language Model Based Agents: A Survey | Mastering the game of go with deep neural networks and tree search. Nat., 529(7587):484â 489, 2016. 68 [361] Kalashnikov, D., A. Irpan, P. Pastor, et al. Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation. CoRR, abs/1806.10293, 2018. [362] Nguyen, H., H. M. La. Review of deep reinforcement learning for robot manipulation. In 3rd IEEE International Conference on Robotic Computing, IRC 2019, Naples, Italy, February 25-27, 2019, pages 590â | 2309.07864#292 | 2309.07864#294 | 2309.07864 | [
"2305.08982"
] |
2309.07864#294 | The Rise and Potential of Large Language Model Based Agents: A Survey | 595. IEEE, 2019. [363] Dasgupta, I., C. Kaeser-Chen, K. Marino, et al. Collaborating with language models for embodied reasoning. CoRR, abs/2302.00763, 2023. [364] Puig, X., K. Ra, M. Boben, et al. Virtualhome: Simulating household activities via programs. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pages 8494â 8502. Computer Vision Foundation / IEEE Computer Society, 2018. [365] Hong, Y., Q. Wu, Y. Qi, et al. | 2309.07864#293 | 2309.07864#295 | 2309.07864 | [
"2305.08982"
] |
2309.07864#295 | The Rise and Potential of Large Language Model Based Agents: A Survey | A recurrent vision-and-language BERT for navigation. CoRR, abs/2011.13922, 2020. [366] Suglia, A., Q. Gao, J. Thomason, et al. Embodied BERT: A transformer model for embodied, language-guided visual task completion. CoRR, abs/2108.04927, 2021. [367] Ganesh, S., N. Vadori, M. Xu, et al. | 2309.07864#294 | 2309.07864#296 | 2309.07864 | [
"2305.08982"
] |
2309.07864#296 | The Rise and Potential of Large Language Model Based Agents: A Survey | Reinforcement learning for market making in a multi-agent dealer market. CoRR, abs/1911.05892, 2019. [368] Tipaldi, M., R. Iervolino, P. R. Massenio. Reinforcement learning in spacecraft control applications: Advances, prospects, and challenges. Annu. Rev. Control., 54:1â 23, 2022. [369] Savva, M., J. Malik, D. Parikh, et al. Habitat: A platform for embodied AI research. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pages 9338â | 2309.07864#295 | 2309.07864#297 | 2309.07864 | [
"2305.08982"
] |
2309.07864#297 | The Rise and Potential of Large Language Model Based Agents: A Survey | 9346. IEEE, 2019. [370] Longpre, S., L. Hou, T. Vu, et al. The flan collection: Designing data and methods for effective instruction tuning. arXiv preprint arXiv:2301.13688, 2023. [371] Wang, Y., Y. Kordi, S. Mishra, et al. Self-instruct: Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560, 2022. [372] Liang, J., W. Huang, F. Xia, et al. | 2309.07864#296 | 2309.07864#298 | 2309.07864 | [
"2305.08982"
] |
2309.07864#298 | The Rise and Potential of Large Language Model Based Agents: A Survey | Code as policies: Language model programs for embodied control. In IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023, pages 9493â 9500. IEEE, 2023. [373] Li, C., F. Xia, R. Martà n-Martà n, et al. HRL4IN: hierarchical reinforcement learning for interactive navigation with mobile manipulators. In L. P. Kaelbling, D. Kragic, K. Sugiura, eds., 3rd Annual Conference on Robot Learning, CoRL 2019, Osaka, Japan, October 30 - November 1, 2019, Proceedings, vol. 100 of Proceedings of Machine Learning Research, pages 603â | 2309.07864#297 | 2309.07864#299 | 2309.07864 | [
"2305.08982"
] |
2309.07864#299 | The Rise and Potential of Large Language Model Based Agents: A Survey | 616. PMLR, 2019. [374] Eppe, M., C. Gumbsch, M. Kerzel, et al. Hierarchical principles of embodied reinforcement learning: A review. CoRR, abs/2012.10147, 2020. [375] Paul, S., A. Roy-Chowdhury, A. Cherian. AVLEN: audio-visual-language embodied navigation in 3d environments. In NeurIPS. 2022. | 2309.07864#298 | 2309.07864#300 | 2309.07864 | [
"2305.08982"
] |
2309.07864#300 | The Rise and Potential of Large Language Model Based Agents: A Survey | [376] Hu, B., C. Zhao, P. Zhang, et al. Enabling intelligent interactions between an agent and an LLM: A reinforcement learning approach. CoRR, abs/2306.03604, 2023. [377] Chen, C., U. Jain, C. Schissler, et al. Soundspaces: Audio-visual navigation in 3d environments. In A. Vedaldi, H. Bischof, T. Brox, J. | 2309.07864#299 | 2309.07864#301 | 2309.07864 | [
"2305.08982"
] |
2309.07864#301 | The Rise and Potential of Large Language Model Based Agents: A Survey | Frahm, eds., Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI, vol. 12351 of Lecture Notes in Computer Science, pages 17â 36. Springer, 2020. [378] Huang, R., Y. Ren, J. Liu, et al. Generspeech: Towards style transfer for generalizable out-of-domain text-to-speech. In NeurIPS. 2022. | 2309.07864#300 | 2309.07864#302 | 2309.07864 | [
"2305.08982"
] |
2309.07864#302 | The Rise and Potential of Large Language Model Based Agents: A Survey | 69 [379] Shah, D., B. Eysenbach, G. Kahn, et al. Ving: Learning open-world navigation with visual goals. In IEEE International Conference on Robotics and Automation, ICRA 2021, Xiâ an, China, May 30 - June 5, 2021, pages 13215â 13222. IEEE, 2021. [380] Huang, C., O. Mees, A. Zeng, et al. | 2309.07864#301 | 2309.07864#303 | 2309.07864 | [
"2305.08982"
] |
2309.07864#303 | The Rise and Potential of Large Language Model Based Agents: A Survey | Visual language maps for robot navigation. In IEEE International Conference on Robotics and Automation, ICRA 2023, London, UK, May 29 - June 2, 2023, pages 10608â 10615. IEEE, 2023. [381] Georgakis, G., K. Schmeckpeper, K. Wanchoo, et al. Cross-modal map learning for vision and language navigation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 15439â | 2309.07864#302 | 2309.07864#304 | 2309.07864 | [
"2305.08982"
] |
2309.07864#304 | The Rise and Potential of Large Language Model Based Agents: A Survey | 15449. IEEE, 2022. [382] Dorbala, V. S., J. F. M. Jr., D. Manocha. Can an embodied agent find your "cat-shaped mug"? llm-based zero-shot object navigation. CoRR, abs/2303.03480, 2023. [383] Li, L. H., P. Zhang, H. Zhang, et al. Grounded language-image pre-training. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 10955â | 2309.07864#303 | 2309.07864#305 | 2309.07864 | [
"2305.08982"
] |
2309.07864#305 | The Rise and Potential of Large Language Model Based Agents: A Survey | 10965. IEEE, 2022. [384] Gan, C., Y. Zhang, J. Wu, et al. Look, listen, and act: Towards audio-visual embodied navigation. In 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31 - August 31, 2020, pages 9701â 9707. IEEE, 2020. [385] Brohan, A., N. Brown, J. Carbajal, et al. | 2309.07864#304 | 2309.07864#306 | 2309.07864 | [
"2305.08982"
] |
2309.07864#306 | The Rise and Potential of Large Language Model Based Agents: A Survey | RT-1: robotics transformer for real-world control at scale. CoRR, abs/2212.06817, 2022. [386] â . RT-2: vision-language-action models transfer web knowledge to robotic control. CoRR, abs/2307.15818, 2023. [387] PrismarineJS, 2013. [388] Gur, I., H. Furuta, A. Huang, et al. A real-world webagent with planning, long context understanding, and program synthesis. CoRR, abs/2307.12856, 2023. [389] Deng, X., Y. Gu, B. Zheng, et al. Mind2web: Towards a generalist agent for the web. | 2309.07864#305 | 2309.07864#307 | 2309.07864 | [
"2305.08982"
] |
2309.07864#307 | The Rise and Potential of Large Language Model Based Agents: A Survey | CoRR, abs/2306.06070, 2023. [390] Furuta, H., O. Nachum, K. Lee, et al. Multimodal web navigation with instruction-finetuned foundation models. CoRR, abs/2305.11854, 2023. [391] Zhou, S., F. F. Xu, H. Zhu, et al. Webarena: A realistic web environment for building autonomous agents. CoRR, abs/2307.13854, 2023. [392] Yao, S., H. Chen, J. Yang, et al. | 2309.07864#306 | 2309.07864#308 | 2309.07864 | [
"2305.08982"
] |
2309.07864#308 | The Rise and Potential of Large Language Model Based Agents: A Survey | Webshop: Towards scalable real-world web interaction with grounded language agents. In NeurIPS. 2022. [393] Kim, G., P. Baldi, S. McAleer. Language models can solve computer tasks. CoRR, abs/2303.17491, 2023. [394] Zheng, L., R. Wang, B. An. Synapse: Leveraging few-shot exemplars for human-level computer control. CoRR, abs/2306.07863, 2023. [395] Chen, P., C. Chang. Interact: Exploring the potentials of chatgpt as a cooperative agent. CoRR, abs/2308.01552, 2023. [396] Gramopadhye, M., D. | 2309.07864#307 | 2309.07864#309 | 2309.07864 | [
"2305.08982"
] |
2309.07864#309 | The Rise and Potential of Large Language Model Based Agents: A Survey | Szafir. Generating executable action plans with environmentally-aware language models. CoRR, abs/2210.04964, 2022. [397] Li, H., Y. Hao, Y. Zhai, et al. The hitchhikerâ s guide to program analysis: A journey with large language models. CoRR, abs/2308.00245, 2023. [398] Feldt, R., S. Kang, J. Yoon, et al. | 2309.07864#308 | 2309.07864#310 | 2309.07864 | [
"2305.08982"
] |
2309.07864#310 | The Rise and Potential of Large Language Model Based Agents: A Survey | Towards autonomous testing agents via conversational large language models. CoRR, abs/2306.05152, 2023. 70 [399] Kang, Y., J. Kim. Chatmof: An autonomous AI system for predicting and generating metal- organic frameworks. CoRR, abs/2308.01423, 2023. [400] Wang, R., P. A. Jansen, M. Côté, et al. Scienceworld: Is your agent smarter than a 5th grader? In Y. Goldberg, Z. Kozareva, Y. Zhang, eds., Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pages 11279â 11298. Association for Computational Linguistics, 2022. [401] Yuan, H., C. Zhang, H. Wang, et al. | 2309.07864#309 | 2309.07864#311 | 2309.07864 | [
"2305.08982"
] |
2309.07864#311 | The Rise and Potential of Large Language Model Based Agents: A Survey | Plan4mc: Skill reinforcement learning and planning for open-world minecraft tasks. CoRR, abs/2303.16563, 2023. [402] Hao, R., L. Hu, W. Qi, et al. Chatllm network: More brains, more intelligence. CoRR, abs/2304.12998, 2023. [403] Mandi, Z., S. Jain, S. Song. Roco: Dialectic multi-robot collaboration with large language models. CoRR, abs/2307.04738, 2023. | 2309.07864#310 | 2309.07864#312 | 2309.07864 | [
"2305.08982"
] |
2309.07864#312 | The Rise and Potential of Large Language Model Based Agents: A Survey | [404] Hamilton, S. Blind judgement: Agent-based supreme court modelling with GPT. CoRR, abs/2301.05327, 2023. [405] Hong, S., X. Zheng, J. Chen, et al. Metagpt: Meta programming for multi-agent collaborative framework. CoRR, abs/2308.00352, 2023. [406] Wu, Q., G. Bansal, J. Zhang, et al. Autogen: Enabling next-gen LLM applications via multi-agent conversation framework. CoRR, abs/2308.08155, 2023. [407] Zhang, C., K. Yang, S. Hu, et al. Proagent: Building proactive cooperative AI with large language models. CoRR, abs/2308.11339, 2023. [408] Nair, V., E. Schumacher, G. J. Tso, et al. | 2309.07864#311 | 2309.07864#313 | 2309.07864 | [
"2305.08982"
] |
2309.07864#313 | The Rise and Potential of Large Language Model Based Agents: A Survey | DERA: enhancing large language model completions with dialog-enabled resolving agents. CoRR, abs/2303.17071, 2023. [409] Talebirad, Y., A. Nadiri. Multi-agent collaboration: Harnessing the power of intelligent LLM agents. CoRR, abs/2306.03314, 2023. [410] Chen, W., Y. Su, J. Zuo, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents. CoRR, abs/2308.10848, 2023. [411] Shi, J., J. Zhao, Y. Wang, et al. | 2309.07864#312 | 2309.07864#314 | 2309.07864 | [
"2305.08982"
] |
2309.07864#314 | The Rise and Potential of Large Language Model Based Agents: A Survey | CGMI: configurable general multi-agent interaction framework. CoRR, abs/2308.12503, 2023. [412] Xiong, K., X. Ding, Y. Cao, et al. Examining the inter-consistency of large language models: An in-depth analysis via debate. CoRR, abs/2305.11595, 2023. [413] Kalvakurthi, V., A. S. Varde, J. Jenq. | 2309.07864#313 | 2309.07864#315 | 2309.07864 | [
"2305.08982"
] |
2309.07864#315 | The Rise and Potential of Large Language Model Based Agents: A Survey | Hey dona! can you help me with student course registration? CoRR, abs/2303.13548, 2023. [414] Swan, M., T. Kido, E. Roland, et al. Math agents: Computational infrastructure, mathematical embedding, and genomics. CoRR, abs/2307.02502, 2023. [415] Hsu, S.-L., R. S. Shah, P. Senthil, et al. Helping the helper: Supporting peer counselors via ai-empowered practice and feedback. arXiv preprint arXiv:2305.08982, 2023. [416] Zhang, H., J. Chen, F. Jiang, et al. Huatuogpt, towards taming language model to be a doctor. CoRR, abs/2305.15075, 2023. [417] Yang, S., H. Zhao, S. Zhu, et al. Zhongjing: Enhancing the chinese medical capabilities of large language model through expert feedback and real-world multi-turn dialogue. | 2309.07864#314 | 2309.07864#316 | 2309.07864 | [
"2305.08982"
] |
2309.07864#316 | The Rise and Potential of Large Language Model Based Agents: A Survey | CoRR, abs/2308.03549, 2023. [418] Ali, M. R., S. Z. Razavi, R. Langevin, et al. A virtual conversational agent for teens with autism spectrum disorder: Experimental results and design lessons. In S. Marsella, R. Jack, H. H. Vilhjálmsson, P. Sequeira, E. S. Cross, eds., IVA â 20: ACM International Conference on Intelligent Virtual Agents, Virtual Event, Scotland, UK, October 20-22, 2020, pages 2:1â 2:8. ACM, 2020. | 2309.07864#315 | 2309.07864#317 | 2309.07864 | [
"2305.08982"
] |
2309.07864#317 | The Rise and Potential of Large Language Model Based Agents: A Survey | 71 [419] Gao, W., X. Gao, Y. Tang. Multi-turn dialogue agent as salesâ assistant in telemarketing. In International Joint Conference on Neural Networks, IJCNN 2023, Gold Coast, Australia, June 18-23, 2023, pages 1â 9. IEEE, 2023. [420] Schick, T., J. A. Yu, Z. Jiang, et al. PEER: A collaborative language model. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023. [421] Lu, B., N. Haduong, C. Lee, et al. DIALGEN: collaborative human-lm generated dialogues for improved understanding of human-human conversations. CoRR, abs/2307.07047, 2023. [422] Gao, D., L. Ji, L. Zhou, et al. Assistgpt: A general multi-modal assistant that can plan, execute, inspect, and learn. | 2309.07864#316 | 2309.07864#318 | 2309.07864 | [
"2305.08982"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.