--- 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:

### 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 script to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that the script is NOT applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct). 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='instruction-pretrain/medicine-Llama3-8B' # 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=True # 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} } ```