--- 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 --- # Adapting Large Language Models to Domains via Continual Pre-Training 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**. ### 🤗 [2024/6/21] We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! 🤗 **************************** **Updates** **************************** * 2024/6/21: 👏🏻 Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) 👏🏻 * 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:

### 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) - [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) The other datasets used in our paper have already been available in huggingface, and 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') # USMLE: dataset=load_dataset('GBaker/MedQA-USMLE-4-options') # 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} } ```