language:
- en
license: apache-2.0
pretty_name: rare disease corpus
tags:
- rare disease corpus
- rare-disease corpus
- rare-disease database
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: content
dtype: string
- name: contents
dtype: string
- name: nordid
dtype: int64
- name: rare-disease
dtype: string
splits:
- name: train
num_bytes: 34808885
num_examples: 9268
download_size: 17060625
dataset_size: 34808885
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
Dataset Card for ReCOP
What's for?
The data for ReCOP is sourced from the National Organization for Rare Disorders (NORD) database, which compiles reports on rare diseases. NORD is committed to the identification, treatment, and cure of rare diseases through education, advocacy, research, and service programs. The primary objective of developing ReCOP using the NORD database is to provide comprehensive expertise on rare diseases for LLMs. This expertise can be leveraged to enhance the diagnostic capabilities of LLMs through retrieval-augmented generation.
Corpus Overview
ReCOP divides each rare disease report into chunks: overview, symptoms, causes, effects, related disorders, diagnosis, and standard therapies. Each property of the disease corresponds to a specific chunk in ReCOP. In this manner, ReCOP generates 9268 chunks based on the reports of 1324 rare diseases for the NORD database, with each report producing seven chunks corresponding to the properties of a rare disease.
Using ReCOP for Retrieval Augmentation Generations
Simply follow our benchmark repository ReDis-QA-Bench to run the retrieval augmentation generations on the ReDis-QA dataset:
git clone https://github.com/guanchuwang/redis-bench.git
cd redis-bench
bash rag-bench/scripts/run_exp.sh
Benchmark Results of Retrieval Augmentation Generations
Benchmark results of retrieval augmentation generations based on ReCOP, where the LLMs take Llama-2-7B-chat, Mistral-7B-instruct-v0.2, Phi-3-7B-instruct, Gemmma-1.1-7B-it, and Qwen-2-7B-Instruct.
Citation Information
If you find this corpus useful to your project, we appreciate you citing this work:
@article{wang2024assessing,
title={Assessing and Enhancing Large Language Models in Rare Disease Question-answering},
author={Wang, Guanchu and Ran, Junhao and Tang, Ruixiang and Chang, Chia-Yuan and Chuang, Yu-Neng and Liu, Zirui and Braverman, Vladimir and Liu, Zhandong and Hu, Xia},
journal={arXiv preprint arXiv:2408.08422},
year={2024}
}