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    <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 and often noisy. 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 show that batch composition also materially affects relevance judgments. 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.</abstract>
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%0 Conference Proceedings
%T Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
%A Huang, Jerry
%A Madala, Siddarth
%A Niu, Cheng
%A Hockenmaier, Julia
%A Zhang, Tong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F huang-etal-2026-contextual
%X 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 and often noisy. 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 show that batch composition also materially affects relevance judgments. 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.
%U https://aclanthology.org/2026.acl-long.94/
%P 2076-2089

Markdown (Informal)

[Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking](https://aclanthology.org/2026.acl-long.94/) (Huang et al., ACL 2026)

• Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking (Huang et al., ACL 2026)

ACL

• Jerry Huang, Siddarth Madala, Cheng Niu, Julia Hockenmaier, and Tong Zhang. 2026. Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2076–2089, San Diego, California, United States. Association for Computational Linguistics.