|
--- |
|
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 |
|
--- |
|
|
|
# Domain Adaptation of Large Language Models |
|
This repo contains the **Biomedicine Knowledge Probing 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**. |
|
|
|
### π€ [2024/6/21] We release the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain), effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! π€ |
|
|
|
**************************** **Updates** **************************** |
|
* 2024/6/21: ππ» Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) ππ» |
|
* 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) of all the evaluation datasets |
|
* 2024/1/16: π Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!π |
|
* 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 LLMs |
|
### 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. |
|
|
|
### 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} |
|
} |
|
``` |
|
|