@inproceedings{do-etal-2026-beyond,
title = "Beyond Sampling: Self-Sorting for Long-Context Ranking",
author = "Do, Juseon and
Han, Sungwoo and
Kwon, Jingun and
Kamigaito, Hidetaka and
Hayashi, Katsuhiko and
Watanabe, Taro",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-eacl.256/",
pages = "4901--4910",
ISBN = "979-8-89176-386-9",
abstract = "Ranking is a fundamental component in a wide range of AI applications. However, large language models (LLMs) remain unstable on long-context ranking. Sliding-window processing is costly and listwise prompting over full candidates still yields inconsistent orders. We show that sampling alone, even with selection-based methods, cannot stabilize ranking because LLM consistency decomposes into within-list order and cross-list preference, in which a single stochastic process cannot align. To address this, we introduce Self-Sorting (SS), which generates m candidate lists and performs n selection-time re-rankings over those lists. SS fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. Experimental results on five widely used ranking benchmarks show significant improvements in nDCG@{1,5,10}, highlighting the critical role of implicit consistency."
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<abstract>Ranking is a fundamental component in a wide range of AI applications. However, large language models (LLMs) remain unstable on long-context ranking. Sliding-window processing is costly and listwise prompting over full candidates still yields inconsistent orders. We show that sampling alone, even with selection-based methods, cannot stabilize ranking because LLM consistency decomposes into within-list order and cross-list preference, in which a single stochastic process cannot align. To address this, we introduce Self-Sorting (SS), which generates m candidate lists and performs n selection-time re-rankings over those lists. SS fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. Experimental results on five widely used ranking benchmarks show significant improvements in nDCG@1,5,10, highlighting the critical role of implicit consistency.</abstract>
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%0 Conference Proceedings
%T Beyond Sampling: Self-Sorting for Long-Context Ranking
%A Do, Juseon
%A Han, Sungwoo
%A Kwon, Jingun
%A Kamigaito, Hidetaka
%A Hayashi, Katsuhiko
%A Watanabe, Taro
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Findings of the Association for Computational Linguistics: EACL 2026
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-386-9
%F do-etal-2026-beyond
%X Ranking is a fundamental component in a wide range of AI applications. However, large language models (LLMs) remain unstable on long-context ranking. Sliding-window processing is costly and listwise prompting over full candidates still yields inconsistent orders. We show that sampling alone, even with selection-based methods, cannot stabilize ranking because LLM consistency decomposes into within-list order and cross-list preference, in which a single stochastic process cannot align. To address this, we introduce Self-Sorting (SS), which generates m candidate lists and performs n selection-time re-rankings over those lists. SS fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. Experimental results on five widely used ranking benchmarks show significant improvements in nDCG@1,5,10, highlighting the critical role of implicit consistency.
%U https://aclanthology.org/2026.findings-eacl.256/
%P 4901-4910
Markdown (Informal)
[Beyond Sampling: Self-Sorting for Long-Context Ranking](https://aclanthology.org/2026.findings-eacl.256/) (Do et al., Findings 2026)
ACL
- Juseon Do, Sungwoo Han, Jingun Kwon, Hidetaka Kamigaito, Katsuhiko Hayashi, and Taro Watanabe. 2026. Beyond Sampling: Self-Sorting for Long-Context Ranking. In Findings of the Association for Computational Linguistics: EACL 2026, pages 4901–4910, Rabat, Morocco. Association for Computational Linguistics.