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        <title>ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation</title>
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        <namePart type="given">Hyeong</namePart>
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    <abstract>Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open-ended tasks where no canonical answer exists. While Best-of-N and self-consistency methods show that aggregating multiple generations can improve performance, existing approaches typically rely on external evaluators, reward models, or exact string-match voting, limiting their applicability and efficiency. We propose Mode Extraction (ModeX), an evaluator-free Best-of-N selection framework that generalizes majority voting to open-ended text generation by identifying the modal output representing the dominant semantic consensus among generated texts. ModeX constructs a similarity graph over candidate generations and recursively applies spectral clustering to select a representative centroid, without requiring additional inference or auxiliary models. We further instantiate this selection principle as ModeX Decoding, a drop-in decoding scheme with early pruning for efficiency. Across open-ended tasks—including text summarization, code generation, and mathematical reasoning—our approaches consistently outperform standard single- and multi-path baselines, providing a computationally efficient, drop-in solution for robust open-ended text generation.</abstract>
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%0 Conference Proceedings
%T ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation
%A Choi, Hyeong Kyu
%A Li, Sharon
%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 choi-li-2026-modex
%X Selecting a single high-quality output from multiple stochastic generations remains a fundamental challenge for large language models (LLMs), particularly in open-ended tasks where no canonical answer exists. While Best-of-N and self-consistency methods show that aggregating multiple generations can improve performance, existing approaches typically rely on external evaluators, reward models, or exact string-match voting, limiting their applicability and efficiency. We propose Mode Extraction (ModeX), an evaluator-free Best-of-N selection framework that generalizes majority voting to open-ended text generation by identifying the modal output representing the dominant semantic consensus among generated texts. ModeX constructs a similarity graph over candidate generations and recursively applies spectral clustering to select a representative centroid, without requiring additional inference or auxiliary models. We further instantiate this selection principle as ModeX Decoding, a drop-in decoding scheme with early pruning for efficiency. Across open-ended tasks—including text summarization, code generation, and mathematical reasoning—our approaches consistently outperform standard single- and multi-path baselines, providing a computationally efficient, drop-in solution for robust open-ended text generation.
%U https://aclanthology.org/2026.acl-long.655/
%P 14394-14416

Markdown (Informal)

[ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation](https://aclanthology.org/2026.acl-long.655/) (Choi & Li, ACL 2026)

• ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation (Choi & Li, ACL 2026)

ACL

• Hyeong Kyu Choi and Sharon Li. 2026. ModeX: Evaluator-Free Best-of-N Selection for Open-Ended Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14394–14416, San Diego, California, United States. Association for Computational Linguistics.