Edit model card

Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis

Paper Link: https://arxiv.org/abs/2407.12857

Project Page: https://ecnu-sea.github.io/

πŸ”₯ News

  • πŸ”₯πŸ”₯πŸ”₯ We have made SEA series models (7B) public !

Model Description

⚠️ This is the SEA-S model for content standardization, and the review model SEA-E can be found here.

The SEA-S model aims to integrate all reviews for each paper into one to eliminate redundancy and errors, focusing on the major advantages and disadvantages of the paper. Specifically, we first utilize GPT-4 to integrate multiple reviews of a paper into one (From ECNU-SEA/SEA_data) that is in a unified format and criterion with constructive contents, and form an instruction dataset for SFT. After that, we fine-tune Mistral-7B-Instruct-v0.2 to distill the knowledge of GPT-4. Therefore, SEA-S provides a novel paradigm for integrating peer review data in an unified format across various conferences.

@misc{yu2024automatedpeerreviewingpaper,
      title={Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis}, 
      author={Jianxiang Yu and Zichen Ding and Jiaqi Tan and Kangyang Luo and Zhenmin Weng and Chenghua Gong and Long Zeng and Renjing Cui and Chengcheng Han and Qiushi Sun and Zhiyong Wu and Yunshi Lan and Xiang Li},
      year={2024},
      eprint={2407.12857},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.12857}, 
}
Downloads last month
24
Safetensors
Model size
7.24B params
Tensor type
BF16
Β·
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train ECNU-SEA/SEA-S