metadata

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.

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, effective for both pre-training from scratch and continual pre-training 🤗

**************************** Updates ****************************

2024/8/29: Updated guidelines on evaluating any 🤗Huggingface models on the domain-specific tasks
2024/6/22: Released the benchmarking code
2024/6/21: Released the 2nd version of AdaptLLM at Instruction-Pretrain
2024/4/14: Released the knowledge probing datasets at med_knowledge_prob and law_knowledge_prob
2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
2024/1/16: Our research paper has been accepted by ICLR 2024
2023/12/19: Released our 13B base models developed from LLaMA-1-13B
2023/12/8: Released our chat models developed from LLaMA-2-Chat-7B
2023/9/18: Released our paper, code, data, and base models 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, Finance-LLM and 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, Finance-LLM-13B and Law-LLM-13B.

LLaMA-2-Chat

Our method is also effective for aligned models! LLaMA-2-Chat requires a specific data format, 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, Finance-Chat and Law-Chat.

LLaMA-3-8B (💡New!)

In our recent research on Instruction-Pretrain, 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, Biomedicine-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, finance-tasks, and 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

git clone https://github.com/microsoft/LMOps
cd LMOps/adaptllm
pip install -r requirements.txt

2). Evaluate the Model

# 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, RCT, ConvFinQA, FiQA_SA, Headline, NER, FPB

Domain Knowledge Probing

Our pre-processed knowledge probing datasets are available at: med_knowledge_prob and law_knowledge_prob

Citation

If you find our work helpful, please cite us:

@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:

@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}
}