Improve model card: add metadata, paper link and repository information
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by
nielsr
HF Staff
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README.md
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license: apache-2.0
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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base_model: qwen/Qwen3-8B
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tags:
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- academic-rebuttal
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- agentic-framework
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- rl
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---
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# DRPG Judge Model
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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).
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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.
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## Links
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- **Paper:** [DRPG: An Agentic Framework for Academic Rebuttal](https://huggingface.co/papers/2601.18081)
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- **Repository:** [ulab-uiuc/DRPG-RebuttalAgent](https://github.com/ulab-uiuc/DRPG-RebuttalAgent)
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## About DRPG
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DRPG is an agentic framework for automatic academic rebuttal generation that operates through four steps:
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1. **Decompose**: Breaking reviews into atomic concerns.
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2. **Retrieve**: Finding relevant evidence from the paper.
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3. **Plan**: Identifying feasible rebuttal strategies.
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4. **Generate**: Creating targeted responses.
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The Judge Model is used within this pipeline to assess rebuttal quality, achieving performance beyond the average human level in experimental evaluations.
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## Usage
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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.
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## Citation
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If you find this model useful in your research, please cite:
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```bibtex
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@article{han2025drpg,
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title={DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal},
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author={Han, Peixuan and Yu, Yingjie and Xu, Jingjun and You, Jiaxuan},
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journal={arXiv preprint arXiv:2601.18081},
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url={https://arxiv.org/pdf/2601.18081},
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year={2026}
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}
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```
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