AutoRev用图结构检索，为作者生成高质量投稿前反馈。
Graph-Guided Passage Retrieval for Author-Centric Structured Feedback
- 构建论文的分层图结构，融合文本与结构信息进行检索
- 减少大模型输入长度，提升反馈质量，多项指标超越基线
- 适合需要快速改进论文的研究者，尤其关注投稿前优化
获取高质量的投稿前反馈是学术出版流程中的关键瓶颈。我们提出AutoRev，一个自动化的作者中心式反馈系统，在正式同行评审前生成结构化、可操作的指导建议。AutoRev采用基于图的检索增强生成框架，将每篇论文建模为分层文档图，整合文本与结构表示，高效检索关键内容。通过图结构的段落检索，显著降低大语言模型的输入上下文长度，从而提升反馈生成质量。实验表明，AutoRev在多个自动评估指标上显著优于基线，同时在人工评估中表现优异。代码将在论文接受后公开。
原文摘要 · Abstract (English)
Obtaining high-quality, pre-submission feedback is a critical bottleneck in the academic publication lifecycle for researchers. We introduce AutoRev, an automated author-centric feedback system that generates structured, actionable guidance prior to formal peer review. AutoRev employs a graph-based retrieval-augmented generation framework that models each paper as a hierarchical document graph, integrating textual and structural representations to retrieve salient content efficiently. By leveraging graph-based passage retrieval, AutoRev substantially reduces LLM input context length, leading to higher-quality feedback generation. Experimental results demonstrate that AutoRev significantly outperforms baselines across multiple automatic evaluation metrics, while achieving strong performance in human evaluations. Code will be released upon acceptance.
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