Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis
This work addresses the challenge of maintaining review quality in the face of increasing paper submissions, though it is incremental as it builds on existing LLM-based reviewing methods.
The authors tackled the problem of inconsistent and generic automated scientific paper reviews by introducing the SEA framework, which standardizes, evaluates, and analyzes reviews using modules SEA-S, SEA-E, and SEA-A, and demonstrated its effectiveness on datasets from eight venues.
In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.