AdaptLLM/ConvFinQA

Adapting Large Language Models to Domains via Continual Pre-Training

This repo contains the ConvFinQA dataset used in our ICLR 2024 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.

[2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at AdaMLLM, for adapting MLLMs to domains 🤗

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

• 2024/11/29: Released AdaMLLM for adapting MLLMs to domains
• 2024/9/20: Our research paper for Instruction-Pretrain has been accepted by EMNLP 2024
• 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 general version of AdaptLLM at Instruction-Pretrain
• 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
• 2024/1/16: Our research paper for AdaptLLM 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

Domain-Specific LLaMA-1

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.

Domain-Specific 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

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, 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.

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

The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code:

from datasets import load_dataset

# MQP:
dataset = load_dataset('medical_questions_pairs')
# PubmedQA:
dataset = load_dataset('bigbio/pubmed_qa')
# USMLE:
dataset=load_dataset('GBaker/MedQA-USMLE-4-options')
# SCOTUS
dataset = load_dataset("lex_glue", 'scotus')
# CaseHOLD
dataset = load_dataset("lex_glue", 'case_hold')
# UNFAIR-ToS
dataset = load_dataset("lex_glue", 'unfair_tos')

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{ConvFinQA,
  author       = {Zhiyu Chen and
                  Shiyang Li and
                  Charese Smiley and
                  Zhiqiang Ma and
                  Sameena Shah and
                  William Yang Wang},
  title        = {ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational
                  Finance Question Answering},
  booktitle    = {{EMNLP}},
  pages        = {6279--6292},
  publisher    = {Association for Computational Linguistics},
  year         = {2022}
}