|
--- |
|
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 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} |
|
} |
|
``` |
|
|