| --- |
| configs: |
| - config_name: Anaesthesia |
| data_files: |
| - split: test |
| path: Anaesthesia.jsonl |
| - config_name: Anatomy |
| data_files: |
| - split: test |
| path: Anatomy.jsonl |
| - config_name: Biochemistry |
| data_files: |
| - split: test |
| path: Biochemistry.jsonl |
| - config_name: Dental |
| data_files: |
| - split: test |
| path: Dental.jsonl |
| - config_name: ENT |
| data_files: |
| - split: test |
| path: ENT.jsonl |
| - config_name: Forensic Medicine |
| data_files: |
| - split: test |
| path: Forensic Medicine.jsonl |
| - config_name: Gynaecology & Obstetrics |
| data_files: |
| - split: test |
| path: Gynaecology & Obstetrics.jsonl |
| - config_name: Medicine |
| data_files: |
| - split: test |
| path: Medicine.jsonl |
| - config_name: Microbiology |
| data_files: |
| - split: test |
| path: Microbiology.jsonl |
| - config_name: Ophthalmology |
| data_files: |
| - split: test |
| path: Ophthalmology.jsonl |
| - config_name: Orthopedics |
| data_files: |
| - split: test |
| path: Orthopedics.jsonl |
| - config_name: Pathology |
| data_files: |
| - split: test |
| path: Pathology.jsonl |
| - config_name: Pediatrics |
| data_files: |
| - split: test |
| path: Pediatrics.jsonl |
| - config_name: Pharmacology |
| data_files: |
| - split: test |
| path: Pharmacology.jsonl |
| - config_name: Physiology |
| data_files: |
| - split: test |
| path: Physiology.jsonl |
| - config_name: Psychiatry |
| data_files: |
| - split: test |
| path: Psychiatry.jsonl |
| - config_name: Radiology |
| data_files: |
| - split: test |
| path: Radiology.jsonl |
| - config_name: Skin |
| data_files: |
| - split: test |
| path: Skin.jsonl |
| - config_name: Social & Preventive Medicine |
| data_files: |
| - split: test |
| path: Social & Preventive Medicine.jsonl |
| - config_name: Surgery |
| data_files: |
| - split: test |
| path: Surgery.jsonl |
| - config_name: Unknown |
| data_files: |
| - split: test |
| path: Unknown.jsonl |
|
|
| task_categories: |
| - text-classification |
| - question-answering |
| - zero-shot-classification |
| language: |
| - en |
| tags: |
| - medical |
| - chemistry |
| - biology |
| --- |
| |
| # Adapting LLMs to Domains via Continual Pre-Training (ICLR 2024) |
| This repo contains the **Biomedicine Knowledge Probing dataset** used in our 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 pre-training from scratch and continual pre-training 🤗 |
|
|
| **************************** **Updates** **************************** |
| * 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 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) |
| * 2024/4/14: Released the knowledge probing datasets at [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob) |
| * 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ChemProt) 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 |
|
|
|
|
| ## 1. Domain-Specific Models |
| ### 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). |
|
|
| ### 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). |
|
|
| ### LLaMA-3-8B (💡New!) |
| In our recent research on [Instruction-Pretrain](https://huggingface.co/papers/2406.14491), we developed a context-based instruction synthesizer to augment the raw corpora with instruction-response pairs, **enabling Llama3-8B to be comparable to or even outperform Llama3-70B**: [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B), [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B). |
|
|
| ## 2. Domain-Specific Tasks |
|
|
| ### Pre-templatized 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. |
|
|
| ### Evaluating Any Huggingface LMs on Domain-Specific Tasks (💡New!) |
| You can use the following scripts to reproduce our results and evaluate any other Huggingface models on the testing splits: |
|
|
| 1). **Set Up Dependencies** |
| ```bash |
| git clone https://github.com/microsoft/LMOps |
| cd LMOps/adaptllm |
| pip install -r requirements.txt |
| ``` |
|
|
| 2). **Evaluate the Model** |
| ```bash |
| # Select the domain from ['biomedicine', 'finance', 'law'] |
| DOMAIN='biomedicine' |
| |
| # Specify any Huggingface model name (Not applicable to chat models) |
| MODEL='AdaptLLM/medicine-LLM' |
| |
| # Model parallelization: |
| # - Set MODEL_PARALLEL=False if the model fits on a single GPU. |
| # We observe that LMs smaller than 10B always meet this requirement. |
| # - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU. |
| MODEL_PARALLEL=False |
| |
| # Choose the number of GPUs from [1, 2, 4, 8] |
| N_GPU=1 |
| |
| # Whether to add a BOS token at the beginning of the prompt input: |
| # - Set to False for AdaptLLM. |
| # - Set to True for instruction-pretrain models. |
| # If unsure, we recommend setting it to False, as this is suitable for most LMs. |
| add_bos_token=False |
| |
| # Run the evaluation script |
| bash scripts/inference.sh ${DOMAIN} ${MODEL} ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU} |
| ``` |
|
|
| ### 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) |
|
|
| ### Domain Knowledge Probing |
| Our pre-processed knowledge probing datasets are available at: [med_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/med_knowledge_prob) and [law_knowledge_prob](https://huggingface.co/datasets/AdaptLLM/law_knowledge_prob) |
|
|
|
|
| ## 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{MedMCQA, |
| author = {Ankit Pal and |
| Logesh Kumar Umapathi and |
| Malaikannan Sankarasubbu}, |
| title = {MedMCQA: {A} Large-scale Multi-Subject Multi-Choice Dataset for Medical |
| domain Question Answering}, |
| booktitle = {{CHIL}}, |
| series = {Proceedings of Machine Learning Research}, |
| volume = {174}, |
| pages = {248--260}, |
| publisher = {{PMLR}}, |
| year = {2022} |
| } |
| ``` |
|
|
