|
|
--- |
|
|
license: apache-2.0 |
|
|
library_name: transformers |
|
|
pipeline_tag: text-generation |
|
|
base_model: qwen/Qwen3-8B |
|
|
tags: |
|
|
- academic-rebuttal |
|
|
- agentic-framework |
|
|
- rl |
|
|
--- |
|
|
|
|
|
# DRPG Judge Model |
|
|
|
|
|
This repository contains the Judge Model for the **DRPG (Decompose, Retrieve, Plan, Generate)** framework, as introduced in the paper [DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal](https://huggingface.co/papers/2601.18081). |
|
|
|
|
|
The model is specifically designed to evaluate the quality of academic rebuttals. It was trained from **Qwen3-8B** using Reinforcement Learning (RL) to provide accurate and persuasive assessment scores. |
|
|
|
|
|
## Links |
|
|
- **Paper:** [DRPG: An Agentic Framework for Academic Rebuttal](https://huggingface.co/papers/2601.18081) |
|
|
- **Repository:** [ulab-uiuc/DRPG-RebuttalAgent](https://github.com/ulab-uiuc/DRPG-RebuttalAgent) |
|
|
|
|
|
## About DRPG |
|
|
DRPG is an agentic framework for automatic academic rebuttal generation that operates through four steps: |
|
|
1. **Decompose**: Breaking reviews into atomic concerns. |
|
|
2. **Retrieve**: Finding relevant evidence from the paper. |
|
|
3. **Plan**: Identifying feasible rebuttal strategies. |
|
|
4. **Generate**: Creating targeted responses. |
|
|
|
|
|
The Judge Model is used within this pipeline to assess rebuttal quality, achieving performance beyond the average human level in experimental evaluations. |
|
|
|
|
|
## Usage |
|
|
Refer to the official [GitHub repository](https://github.com/ulab-uiuc/DRPG-RebuttalAgent) for instructions on running the evaluation scripts and using the model within the DRPG pipeline. |
|
|
|
|
|
## Citation |
|
|
If you find this model useful in your research, please cite: |
|
|
```bibtex |
|
|
@article{han2025drpg, |
|
|
title={DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal}, |
|
|
author={Han, Peixuan and Yu, Yingjie and Xu, Jingjun and You, Jiaxuan}, |
|
|
journal={arXiv preprint arXiv:2601.18081}, |
|
|
url={https://arxiv.org/pdf/2601.18081}, |
|
|
year={2026} |
|
|
} |
|
|
``` |
