On Listwise Reranking for Corpus Feedback
Soyoung Yoon (Seoul National University)
Jongho Kim (Seoul National University)
Daeyong Kwon (Seoul National University)
Avishek Anand (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Seung Won Hwang (TU Delft - Electrical Engineering, Mathematics and Computer Science, Seoul National University)
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Abstract
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.
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