| | --- |
| | configs: |
| | - config_name: NER |
| | 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 **NER dataset** used in our **ICLR 2024** 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**. |
| |
|
| | ### [2024/11/29] 🤗 Introduce the multimodal version of AdaptLLM at [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains), for adapting MLLMs to domains 🤗 |
| |
|
| | **************************** **Updates** **************************** |
| | * 2024/11/29: Released [AdaMLLM](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains) for adapting MLLMs to domains |
| | * 2024/9/20: Our [research paper for Instruction-Pretrain](https://huggingface.co/papers/2406.14491) has been accepted by EMNLP 2024 |
| | * 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 general version of AdaptLLM at [Instruction-Pretrain](https://huggingface.co/instruction-pretrain) |
| | * 2024/4/2: Released the [raw data splits (train and test)](https://huggingface.co/datasets/AdaptLLM/ConvFinQA) of all the evaluation datasets |
| | * 2024/1/16: Our [research paper for AdaptLLM](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 |
| |
|
| |
|
| | ## 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](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). |
| |
|
| | ## Domain-Specific 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) |
| |
|
| | ## 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](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. |
| |
|
| | ### 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) |
| |
|
| | The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code: |
| | ```python |
| | 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: |
| | ```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{NER, |
| | author = {Julio Cesar Salinas Alvarado and |
| | Karin Verspoor and |
| | Timothy Baldwin}, |
| | title = {Domain Adaption of Named Entity Recognition to Support Credit Risk |
| | Assessment}, |
| | booktitle = {{ALTA}}, |
| | pages = {84--90}, |
| | publisher = {{ACL}}, |
| | year = {2015} |
| | } |
| | ``` |
