metadata

configs:
  - config_name: law_knowledge_prob
    data_files:
      - split: test
        path: test.jsonl
task_categories:
  - text-classification
  - question-answering
  - zero-shot-classification
language:
  - en
tags:
  - legal

Domain Adaptation of Large Language Models

This repo contains the Law Knowledge Probing 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.

🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗

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

2024/4/14: Released the knowledge probing datasets at med_knowledge_prob and law_knowledge_prob
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 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, 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.

Domain Knowledge Probing

Our pre-processed knowledge probing datasets are available at: med_knowledge_prob and law_knowledge_prob

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{LEDGAR,
  author       = {Don Tuggener and
                  Pius von D{\"{a}}niken and
                  Thomas Peetz and
                  Mark Cieliebak},
  title        = {{LEDGAR:} {A} Large-Scale Multi-label Corpus for Text Classification
                  of Legal Provisions in Contracts},
  booktitle    = {{LREC}},
  pages        = {1235--1241},
  publisher    = {European Language Resources Association},
  year         = {2020}
}