| --- |
| configs: |
| - config_name: ChemProt |
| 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 **ChemProt 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**. |
|
|
| ### [2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains), for adapting MLLMs to domains 🤗 |
|
|
| **************************** **Updates** **************************** |
| * 2024/11/29: Released [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains) for adapting MLLMs to domains |
| * 2024/9/20: Our [research paper for Instruction-Pretrain](https://huggingface.co/papers/2406.14491) has been accepted by EMNLP 2024 |
| * 2024/8/29: Updated [guidelines](https://huggingface.co/datasets/AdaptLLM/finance-tasks) on evaluating any 🤗Huggingface models on the domain-specific tasks |
| * 2024/6/22: Released the [benchmarking code](https://github.com/microsoft/LMOps/tree/main/adaptllm) |
| * 2024/6/21: Released the general version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) |
| * 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets |
| * 2024/1/16: Our [research paper for AdaptLLM](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) |
| - [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 |
| @article{ChemProt, |
| author = {Jens Kringelum and |
| Sonny Kim Kj{\ae}rulff and |
| S{\o}ren Brunak and |
| Ole Lund and |
| Tudor I. Oprea and |
| Olivier Taboureau}, |
| title = {ChemProt-3.0: a global chemical biology diseases mapping}, |
| journal = {Database J. Biol. Databases Curation}, |
| volume = {2016}, |
| year = {2016} |
| } |
| ``` |
|
|
