Graph-Guided Passage Retrieval for Author-Centric Structured Feedback
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908754344148992 |
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| author | Chitale, Maitreya Prafulla Shetye, Ketaki Mangesh Gupta, Harshit Chaudhary, Manav Shrivastava, Manish Varma, Vasudeva |
| author_facet | Chitale, Maitreya Prafulla Shetye, Ketaki Mangesh Gupta, Harshit Chaudhary, Manav Shrivastava, Manish Varma, Vasudeva |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-Guided Passage Retrieval for Author-Centric Structured Feedback Chitale, Maitreya Prafulla Shetye, Ketaki Mangesh Gupta, Harshit Chaudhary, Manav Shrivastava, Manish Varma, Vasudeva Computation and Language 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. |
| title | Graph-Guided Passage Retrieval for Author-Centric Structured Feedback |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.14376 |