https://github.com/dmis-lab/bc9-medhop-qa
https://github.com/dmis-lab/bc9-medhop-qa
Last synced: 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/dmis-lab/bc9-medhop-qa
- Owner: dmis-lab
- Created: 2026-02-24T16:31:28.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2026-02-25T06:54:38.000Z (3 months ago)
- Last Synced: 2026-02-25T10:45:43.767Z (3 months ago)
- Size: 360 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DMIS at BioCreative IX MedHopQA Track
This repository contains the code implementation of our methodology for generating answers to multi-step reasoning questions in the healthcare and biomedical domains, as introduced in the BioCreative IX MedhopQA Challenge.
## Overview & Quick Links
* **Task description**: The MedHopQA task requires the generation of answers to multi-step reasoning questions in the healthcare and biomedical domains. For detailed information, please visit to the official BC9 website (**[link](https://www.ncbi.nlm.nih.gov/research/bionlp/medhopqa)**) or see the overview paper (**[PDF](https://zenodo.org/records/16992154)**).
* **System description**: Please check out our paper listed below.
* **[This paper](https://zenodo.org/records/16875789)** provides a brief description of our system.
## Requirements
This repository has been tested on `Python==3.10`, `PyTorch==2.10`, and `Transformers==4.57.6`. See the `requirements.txt` file and instructions below.
```bash
# Download this project
git clone https://github.com/dmis-lab/bc9-medhop-qa.git
cd bc9-medhop-qa
# Create a conda environment
conda create -n bc9 python=3.10
conda activate bc9
# Install all requirements
pip install -r requirements.txt
```
## File Structure
```bash
bc9-medhop-qa
├── core
│ ├── llm
│ │ ├── gemini.py
│ │ ├── gpt_4o.py
│ │ ├── gpt_5_2.py
│ │ └── o3.py
│ ├── medhop_dataset.py
│ ├── reranker
│ │ ├── medcpt_reranker.py
│ │ ├── qwen3_reranker.py
│ │ └── reranker_base.py
│ └── retriever
│ ├── dense_wikipedia.py
│ ├── elastic_wikipedia.py
│ └── retriever_base.py
├── data
│ └── MedHopQA_Test_Dataset.csv
├── decision_making.py
├── output
│ ├── result_decision_making.csv
│ ├── result_q2d.csv
│ ├── result_rag.csv
│ ├── result_rationale.csv
│ └── result_web_search.csv
├── prompt
│ ├── decision_making.py
│ ├── query2doc.py
│ ├── rationale.py
│ └── web_search.py
├── query2doc.py
├── rationale.py
├── README.md
├── utils
│ ├── merge_csvs.py
│ └── normalize_long_answer.py
└── web_search.py
```
## Excecution
```bash
# 1. Run Query2Doc
python query2doc.py
# 2. Run Rationale
python ratinoale.py
# 3. Run Web Search
python web_search.py
# 4. Merge outputs
python utils/merge_csvs.py
# 5. Run decision-making
python decision-making.py
```
* Note: The Elasticsearch server and Wikipedia embeddings required for retrieval must be configured independently.
## References
Please cite the papers below if you use our code, method, or if your work is inspired by ours.
```bash
@inproceedings{jung2025dmis,
author = {Jung, J. and Hwang, H. and Park, Y. and Song, M. and Yoon, J. and Hwang, H. and Lee, S. and Sohn, J. and Kang, J.},
title = {DMIS Lab at MedHopQA-2025: Ensemble Multi-Retrieval Methodologies with Reasoning Language Model Decision},
booktitle = {Proceedings of the BioCreative IX Challenge and Workshop (BC9): Large Language Models for Clinical and Biomedical NLP at the International Joint Conference on Artificial Intelligence (IJCAI)},
year = {2025},
doi = {10.5281/zenodo.16875789},
url = {https://doi.org/10.5281/zenodo.16875789}
}
```
Also, it should be noted that appropriate references must be cited when using the MedHopQA dataset or citing BC9 challenge results, etc.