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arXiv 2410.16640cs.CL

基于谚语的大语言模型自我评估能力的统计分析

A Statistical Analysis of LLMs' Self-Evaluation Using Proverbs

Fujitsu Limited(富士通株式会社);Fujitsu Research of America(美国富士通研究所)

Ryosuke Sonoda, Ramya Srinivasan

AI总结:

本文通过构建300对相似谚语数据库并设计文本与数值一致性测试,统计分析LLMs的自我评估能力,揭示其在性别刻板印象和文化理解方面的不足。

摘要:

大型语言模型(LLMs),如ChatGPT、GPT-4、Claude-3和Llama,正在被广泛应用于各行各业。尽管其迅速普及,专家仍呼吁在解读和采用LLMs时保持谨慎,因为存在诸多伦理问题。研究也揭示了LLMs在推理和逻辑能力方面的不足,这引发了对其作为评估工具潜力的质疑。本文研究LLMs在一项新颖的谚语推理任务上的自我评估能力。我们引入了一个新的谚语数据库,包含300对意图相似但措辞不同的谚语,主题涵盖性别、智慧和社会。我们提出了测试方法来评估相似谚语之间的文本一致性以及数值一致性,并证明了我们的方法和数据集在识别LLMs自我评估失败方面的有效性,这些失败进而可以揭示与性别刻板印象和缺乏文化理解相关的问题。

英文摘要:

Large language models (LLMs) such as ChatGPT, GPT-4, Claude-3, and Llama are being integrated across a variety of industries. Despite this rapid proliferation, experts are calling for caution in the interpretation and adoption of LLMs, owing to numerous associated ethical concerns. Research has also uncovered shortcomings in LLMs' reasoning and logical abilities, raising questions on the potential of LLMs as evaluation tools. In this paper, we investigate LLMs' self-evaluation capabilities on a novel proverb reasoning task. We introduce a novel proverb database consisting of 300 proverb pairs that are similar in intent but different in wordings, across topics spanning gender, wisdom, and society. We propose tests to evaluate textual consistencies as well as numerical consistencies across similar proverbs, and demonstrate the effectiveness of our method and dataset in identifying failures in LLMs' self-evaluation which in turn can highlight issues related to gender stereotypes and lack of cultural understanding in LLMs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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