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English(EN) More Capable, Less Faithful: A Multilingual Analysis of Mathematical (Un)Solvability Detection in LLMs

新的多语言基准评估大型语言模型数学可解性检测能力

arXiv上发表的一项新研究提出了首个用于评估大型语言模型(LLMs)数学可解性检测能力的多语言基准。该基准在现有ReliableMath数据集的基础上,增加了法语和希腊语问题,并保留了英语。研究人员发现,大型语言模型以一种普遍的、与语言无关的方式编码可解性信念,但像英语这样资源更丰富的语言在检测可解性方面表现出较低的忠实度。 AI

影响 这项研究通过强调与语言无关的信念编码和忠实度问题,有望提高多语言大型语言模型中更强大的数学推理能力。

排序理由 该集群包含一篇研究论文,详细介绍了用于评估大型语言模型能力的新的基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的多语言基准评估大型语言模型数学可解性检测能力

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该集群包含一篇研究论文,详细介绍了用于评估大型语言模型能力的新的基准。[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) · Maria-Eleni Zoumpoulidi, Nikolaos Xiros, Georgios Paraskevopoulos ·

    更强大,更不忠实:LLM 数学(不可)解题能力多语言分析

    arXiv:2608.30463v1 Announce Type: new Abstract: Solvability detection is one of the most challenging aspects of mathematical reasoning for Large Language Models (LLMs). While prior work has studied this capability extensively, these analyses have been limited to English. Conseque…