@inproceedings{li-etal-2026-query,
title = "Query-focused and Memory-aware Reranker for Long Context Processing",
author = "Li, Yuqing and
Li, Jiangnan and
Yu, Mo and
Chen, Yanyu and
Ding, Guoxuan and
Lin, Zheng and
Zhang, Wei and
Zhou, Jie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1607/",
pages = "36553--36571",
ISBN = "979-8-89176-459-0",
abstract = "Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage{--}query relevance using the attention scores of selected heads. This approach provides a listwise solution that leverages the holistic information within the entire candidate shortlist during ranking. At the same time, it naturally produces continuous relevance scores, enabling training on arbitrary retrieval datasets without requiring Likert-scale supervision. Our framework is lightweight and effective, requiring only small-scale models (\textit{e.g.}, 4B parameters) to achieve strong performance. Extensive experiments demonstrate that our method outperforms existing state-of-the-art pointwise and listwise rerankers across multiple domains, including Wikipedia and long narrative datasets. It further establishes a new state-of-the-art on the LoCoMo benchmark that assesses the capabilities of dialogue understanding and memory usage. We further demonstrate that our framework supports flexible extensions. For example, augmenting candidate passages with contextual information further improves ranking accuracy, while training attention heads from middle layers enhances efficiency without sacrificing performance."
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<abstract>Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage–query relevance using the attention scores of selected heads. This approach provides a listwise solution that leverages the holistic information within the entire candidate shortlist during ranking. At the same time, it naturally produces continuous relevance scores, enabling training on arbitrary retrieval datasets without requiring Likert-scale supervision. Our framework is lightweight and effective, requiring only small-scale models (e.g., 4B parameters) to achieve strong performance. Extensive experiments demonstrate that our method outperforms existing state-of-the-art pointwise and listwise rerankers across multiple domains, including Wikipedia and long narrative datasets. It further establishes a new state-of-the-art on the LoCoMo benchmark that assesses the capabilities of dialogue understanding and memory usage. We further demonstrate that our framework supports flexible extensions. For example, augmenting candidate passages with contextual information further improves ranking accuracy, while training attention heads from middle layers enhances efficiency without sacrificing performance.</abstract>
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%0 Conference Proceedings
%T Query-focused and Memory-aware Reranker for Long Context Processing
%A Li, Yuqing
%A Li, Jiangnan
%A Yu, Mo
%A Chen, Yanyu
%A Ding, Guoxuan
%A Lin, Zheng
%A Zhang, Wei
%A Zhou, Jie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-query
%X Built upon the existing analysis of retrieval heads in large language models, we propose an alternative reranking framework that trains models to estimate passage–query relevance using the attention scores of selected heads. This approach provides a listwise solution that leverages the holistic information within the entire candidate shortlist during ranking. At the same time, it naturally produces continuous relevance scores, enabling training on arbitrary retrieval datasets without requiring Likert-scale supervision. Our framework is lightweight and effective, requiring only small-scale models (e.g., 4B parameters) to achieve strong performance. Extensive experiments demonstrate that our method outperforms existing state-of-the-art pointwise and listwise rerankers across multiple domains, including Wikipedia and long narrative datasets. It further establishes a new state-of-the-art on the LoCoMo benchmark that assesses the capabilities of dialogue understanding and memory usage. We further demonstrate that our framework supports flexible extensions. For example, augmenting candidate passages with contextual information further improves ranking accuracy, while training attention heads from middle layers enhances efficiency without sacrificing performance.
%U https://aclanthology.org/2026.emnlp-main.1607/
%P 36553-36571
Markdown (Informal)
[Query-focused and Memory-aware Reranker for Long Context Processing](https://aclanthology.org/2026.emnlp-main.1607/) (Li et al., EMNLP 2026)
ACL
- Yuqing Li, Jiangnan Li, Mo Yu, Yanyu Chen, Guoxuan Ding, Zheng Lin, Wei Zhang, and Jie Zhou. 2026. Query-focused and Memory-aware Reranker for Long Context Processing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 36553–36571, Budapest, Hungary. Association for Computational Linguistics.