metadata

configs:
  - config_name: FPB
    data_files:
      - split: train
        path: train.csv
      - split: test
        path: test.csv
task_categories:
  - text-classification
  - question-answering
  - zero-shot-classification
language:
  - en
tags:
  - finance

Adapting Large Language Models to Domains via Continual Pre-Training

This repo contains the FPB 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.

🤗 [2024/6/21] We release the 2nd version of AdaptLLM at Instruction-Pretrain, effective for both general pre-training from scratch and domain-adaptive continual pre-training!!! 🤗

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

2024/6/21: 👏🏻 Released the 2nd 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 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:

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:

@article{FPB,
  author       = {Pekka Malo and
                  Ankur Sinha and
                  Pekka J. Korhonen and
                  Jyrki Wallenius and
                  Pyry Takala},
  title        = {Good debt or bad debt: Detecting semantic orientations in economic
                  texts},
  journal      = {J. Assoc. Inf. Sci. Technol.},
  volume       = {65},
  number       = {4},
  pages        = {782--796},
  year         = {2014}
}