Create README.md

README.md

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+---
+configs:
+- config_name: RCT
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: dev.jsonl
+  - split: test
+    path: test.jsonl
+task_categories:
+- text-classification
+- question-answering
+- zero-shot-classification
+language:
+- en
+tags:
+- medical
+- chemistry
+- biology
+---
+
+# Domain Adaptation of Large Language Models
+This repo contains the **RCT dataset** used in our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
+
+We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**. 
+
+### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
+
+**************************** **Updates** ****************************
+* 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
+* 2024/1/16: 🎉 Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!🎉
+* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B.
+* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B.
+* 2023/9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/law-tasks), and [base models](https://huggingface.co/AdaptLLM/law-LLM) developed from LLaMA-1-7B.
+
+
+## Domain-Specific LLaMA-1
+### LLaMA-1-7B
+In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
+
+<p align='center'>
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
+</p>
+
+### LLaMA-1-13B
+Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
+
+## Domain-Specific LLaMA-2-Chat
+Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
+
+## Domain-Specific Tasks
+
+### Pre-templatized/Formatted Testing Splits
+To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
+
+**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
+
+### Raw Datasets
+We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: 
+- [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt)
+- [RCT](https://huggingface.co/datasets/AdaptLLM/RCT)
+- [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA)
+- [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA)
+- [Headline](https://huggingface.co/datasets/AdaptLLM/Headline)
+- [NER](https://huggingface.co/datasets/AdaptLLM/NER)
+
+The other datasets used in our paper have already been available in huggingface, so you can directly load them with the following code
+```python
+from datasets import load_dataset
+
+# MQP:
+dataset = load_dataset('medical_questions_pairs')
+
+# PubmedQA:
+dataset = load_dataset('bigbio/pubmed_qa')
+
+# SCOTUS
+dataset = load_dataset("lex_glue", 'scotus')
+
+# CaseHOLD
+dataset = load_dataset("lex_glue", 'case_hold')
+
+# UNFAIR-ToS
+dataset = load_dataset("lex_glue", 'unfair_tos')
+```
+
+## Citation
+If you find our work helpful, please cite us:
+```bibtex
+@inproceedings{
+cheng2024adapting,
+title={Adapting Large Language Models via Reading Comprehension},
+author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+booktitle={The Twelfth International Conference on Learning Representations},
+year={2024},
+url={https://openreview.net/forum?id=y886UXPEZ0}
+}
+```
+
+and the original dataset:
+```bibtex
+@inproceedings{RCT,
+  author    = {Franck Dernoncourt and
+               Ji Young Lee},
+  title     = {PubMed 200k {RCT:} a Dataset for Sequential Sentence Classification
+               in Medical Abstracts},
+  booktitle = {{IJCNLP}},
+  pages     = {308--313},
+  publisher = {Asian Federation of Natural Language Processing},
+  year      = {2017}
+}
+```