On Listwise Reranking for Corpus Feedback
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914211582443520 |
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| author | Yoon, Soyoung Kim, Jongho Kwon, Daeyong Anand, Avishek Hwang, Seung-won |
| author_facet | Yoon, Soyoung Kim, Jongho Kwon, Daeyong Anand, Avishek Hwang, Seung-won |
| contents | Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces document graphs from listwise reranker logs. By converting reranker signals into a graph structure, L2G enables scalable graph-based retrieval without the overhead of explicit graph computation. Results on the TREC-DL and BEIR subset show that L2G matches the effectiveness of oracle-based graph methods, while incurring zero additional LLM calls. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_00887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Listwise Reranking for Corpus Feedback Yoon, Soyoung Kim, Jongho Kwon, Daeyong Anand, Avishek Hwang, Seung-won Information Retrieval Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces document graphs from listwise reranker logs. By converting reranker signals into a graph structure, L2G enables scalable graph-based retrieval without the overhead of explicit graph computation. Results on the TREC-DL and BEIR subset show that L2G matches the effectiveness of oracle-based graph methods, while incurring zero additional LLM calls. |
| title | On Listwise Reranking for Corpus Feedback |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2510.00887 |