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--Lite, an improved version of ModeX 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 solution for robust open-ended text generation. Code is released in this https URL.
Abstract (translated)
从多个随机生成的结果中选择一个高质量的输出，仍然是大型语言模型（LLM）面临的基本挑战，尤其是在没有标准答案存在的开放性任务中。虽然Best-of-N和自我一致性方法表明聚合多次生成可以提高性能，但现有的方法通常依赖于外部评估者、奖励模型或精确字符串匹配投票，这限制了它们的应用范围和效率。我们提出了模式提取（ModeX），这是一种无需评估器的Best-of-N选择框架，通过识别生成文本中代表主导语义共识的模态输出，将多数表决推广到了开放性文本生成任务中。ModeX构建了一个候选生成之间的相似性图，并递归地应用谱聚类来选择一个代表性中心点，而不需要额外的推理或辅助模型。我们进一步实现为ModeX-Lite，这是模式提取的一个改进版本，通过早期剪枝提高了效率。在包括文本摘要、代码生成和数学推理在内的开放性任务中，我们的方法始终优于标准单路径或多路径基线，提供了一种计算上高效且稳健的解决方案用于开放性文本生成。相关代码可以在提供的URL地址获取。
URL
https://arxiv.org/abs/2601.02535
