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English(EN) CGES: Confidence-Guided Early Stopping for Efficient and Accurate Self-Consistency

新的CGES方法将LLM调用次数减少58%，同时保持准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个新的贝叶斯框架，称为置信度引导的提前停止（CGES），以提高大型语言模型（LLM）查询的效率。与需要固定调用次数的传统自洽性方法不同，CGES在单个答案获得足够置信度后即可自适应地停止采样。这种方法显著减少了所需的LLM调用次数，在五个推理基准测试中平均减少了58%，同时保持了与标准自洽性策略相当的准确性。 AI

影响 降低了LLM推理的计算成本，可能有助于更广泛地部署复杂的推理任务。

排序理由 详细介绍LLM查询新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的CGES方法将LLM调用次数减少58%，同时保持准确性

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详细介绍LLM查询新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ehsan Aghazadeh, Ahmad Ghasemi, Hedyeh Beyhaghi, Hossein Pishro-Nik ·

    CGES：用于高效准确自洽的置信度引导早期停止

    arXiv:2511.02603v2 Announce Type: replace Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self-consistency (Wang et al., 2023) strategy requires a fixed number of calls and f…

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