@inproceedings{choi-li-2026-modex,
    title = "{M}ode{X}: Evaluator-Free Best-of-N Selection for Open-Ended Generation",
    author = "Choi, Hyeong Kyu  and
      Li, Sharon",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.655/",
    pages = "14394--14416",
    ISBN = "979-8-89176-390-6",
    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."
}