Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking

• URL: http://arxiv.org/abs/2511.01208v1
• Date: Mon, 03 Nov 2025 04:03:32 GMT
• Title: Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
• Authors: Jerry Huang, Siddarth Madala, Cheng Niu, Julia Hockenmaier, Tong Zhang,
• Abstract summary: We propose contextual relevance, which we define as the probability that a document is relevant to a given query.<n>To efficiently estimate contextual relevance, we propose TS-SetRank, a sampling-based, uncertainty-aware reranking algorithm.<n> Empirically, TS-SetRank improves nDCG@10 over retrieval and reranking baselines by 15-25% on BRIGHT and 6-21% on BEIR.
• Score: 16.036042734987024
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reranking algorithms have made progress in improving document retrieval quality by efficiently aggregating relevance judgments generated by large language models (LLMs). However, identifying relevant documents for queries that require in-depth reasoning remains a major challenge. Reasoning-intensive queries often exhibit multifaceted information needs and nuanced interpretations, rendering document relevance inherently context dependent. To address this, we propose contextual relevance, which we define as the probability that a document is relevant to a given query, marginalized over the distribution of different reranking contexts it may appear in (i.e., the set of candidate documents it is ranked alongside and the order in which the documents are presented to a reranking model). While prior works have studied methods to mitigate the positional bias LLMs exhibit by accounting for the ordering of documents, we empirically find that the compositions of these batches also plays an important role in reranking performance. To efficiently estimate contextual relevance, we propose TS-SetRank, a sampling-based, uncertainty-aware reranking algorithm. Empirically, TS-SetRank improves nDCG@10 over retrieval and reranking baselines by 15-25% on BRIGHT and 6-21% on BEIR, highlighting the importance of modeling relevance as context-dependent.

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