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--- |
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configs: |
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- config_name: ConvFinQA |
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data_files: |
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- split: train |
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path: train_turn.json |
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- split: validation |
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path: dev_turn.json |
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task_categories: |
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- text-classification |
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- question-answering |
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- zero-shot-classification |
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language: |
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- en |
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tags: |
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- finance |
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--- |
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# Adapting Large Language Models to Domains via Continual Pre-Training |
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This repo contains the **ConvFinQA dataset** used in our **ICLR 2024** paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530). |
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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**. |
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### 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€ |
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**************************** **Updates** **************************** |
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* 2024/6/21: ππ» Released the 2nd version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) ππ» |
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* 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets |
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* 2024/1/16: π Our [research paper](https://huggingface.co/papers/2309.09530) has been accepted by ICLR 2024!!!π |
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* 2023/12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/law-LLM-13B) developed from LLaMA-1-13B. |
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* 2023/12/8: Released our [chat models](https://huggingface.co/AdaptLLM/law-chat) developed from LLaMA-2-Chat-7B. |
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* 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. |
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## Domain-Specific LLaMA-1 |
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### LLaMA-1-7B |
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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: |
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<p align='center'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700"> |
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</p> |
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### LLaMA-1-13B |
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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). |
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## Domain-Specific LLaMA-2-Chat |
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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) |
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## Domain-Specific Tasks |
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### Pre-templatized/Formatted Testing Splits |
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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). |
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**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. |
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### Raw Datasets |
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We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages: |
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- [ChemProt](https://huggingface.co/datasets/AdaptLLM/ChemProt) |
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- [RCT](https://huggingface.co/datasets/AdaptLLM/RCT) |
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- [ConvFinQA](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) |
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- [FiQA_SA](https://huggingface.co/datasets/AdaptLLM/FiQA_SA) |
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- [Headline](https://huggingface.co/datasets/AdaptLLM/Headline) |
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- [NER](https://huggingface.co/datasets/AdaptLLM/NER) |
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- [FPB](https://huggingface.co/datasets/AdaptLLM/FPB) |
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The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code: |
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```python |
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from datasets import load_dataset |
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# MQP: |
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dataset = load_dataset('medical_questions_pairs') |
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# PubmedQA: |
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dataset = load_dataset('bigbio/pubmed_qa') |
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# USMLE: |
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dataset=load_dataset('GBaker/MedQA-USMLE-4-options') |
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# SCOTUS |
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dataset = load_dataset("lex_glue", 'scotus') |
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# CaseHOLD |
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dataset = load_dataset("lex_glue", 'case_hold') |
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# UNFAIR-ToS |
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dataset = load_dataset("lex_glue", 'unfair_tos') |
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``` |
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## Citation |
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If you find our work helpful, please cite us: |
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```bibtex |
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@inproceedings{ |
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cheng2024adapting, |
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title={Adapting Large Language Models via Reading Comprehension}, |
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author={Daixuan Cheng and Shaohan Huang and Furu Wei}, |
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booktitle={The Twelfth International Conference on Learning Representations}, |
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year={2024}, |
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url={https://openreview.net/forum?id=y886UXPEZ0} |
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} |
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``` |
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and the original dataset: |
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```bibtex |
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@inproceedings{ConvFinQA, |
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author = {Zhiyu Chen and |
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Shiyang Li and |
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Charese Smiley and |
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Zhiqiang Ma and |
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Sameena Shah and |
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William Yang Wang}, |
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title = {ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational |
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Finance Question Answering}, |
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booktitle = {{EMNLP}}, |
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pages = {6279--6292}, |
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publisher = {Association for Computational Linguistics}, |
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year = {2022} |
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} |
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``` |