通过自信度与词汇覆盖度，提前剪枝冗余推理路径，提升大模型长链条推理的效率。
Confidence-Weighted Token Set Cover for Early Hypothesis Pruning in Self-Consistency
- 基于模型自信度和候选假设的词汇覆盖度，动态剪枝低效推理路径。
- 在多个数学基准上，各模型平均节省10%-35%的生成令牌数。
- 适合追求高效推理的场景，尤其适用于长链条思维链任务。
尽管自一致性方法简单有效，但其高昂的令牌开销限制了实际应用。本文研究如何通过早期假设剪枝，提升自一致性在长链思维链推理任务中的令牌效率，同时保持并行性。具体而言，所有解法并行生成，但定期根据两个轻量级指标剪枝无用的中间假设：(a) 模型对单个假设的自身置信度；(b) 当前假设集合对候选保留子集的词汇覆盖程度。设计了一种快速加权集合覆盖算法，利用这两个指标。在五个大语言模型和三个数学基准上的评估表明，该方法在多数情况下可使所有模型的令牌效率提升10%-35%。
原文摘要 · Abstract (English)
Despite its simplicity and efficacy, the high token expenditure of self-consistency can limit its practical utility. Here we investigate if self-consistency can be made more token-efficient for long chain-of-thought reasoning tasks, while preserving its parallelism, through early hypothesis pruning. Concretely, we generate all solutions in parallel, but periodically prune intermediate hypotheses that are deemed unnecessary based on two lightweight indicators: (a) the model's own confidence in individual hypotheses, and (b) lexical coverage of all current hypotheses by candidate subsets that are under consideration for continued retention. We design a fast weighted set cover algorithm that utilizes the two indicators; our evaluation of five LLMs on three math benchmarks shows that this method can improve token efficiency for all models, by 10-35% in many cases.
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