README.md

---
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}
}
```