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arXiv 2502.08045cs.CLcs.AIcs.CY
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打破复选框:挑战大语言模型文化对齐的封闭式评估

Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

Department of Computer Science, National Center for Text Mining, The University of Manchester(计算机科学系,文本挖掘国家中心,曼彻斯特大学);Department of Computer Science and Engineering, Islamic University of Technology(计算机科学与工程系,伊斯兰技术大学)

Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou

AI总结:

本文针对现有大语言模型文化对齐依赖封闭式多选评估的局限,通过WVS和霍夫斯泰德文化维度案例验证该范式的缺陷,倡导采用更灵活稳健的评估框架以实现更准确的文化对齐评估。

摘要:

大量研究依赖封闭式多选调查来评估大语言模型(LLMs)的文化对齐能力。本研究对这种受限评估范式提出质疑,探索更贴近现实的非约束性评估方法。研究以世界价值观调查(WVS)和霍夫斯泰德文化维度为案例,证明在无强制作答的非约束性场景下,LLMs表现出更强的文化对齐能力。此外,研究发现哪怕是调整选项顺序这类微小改动,都会导致输出结果不一致,暴露出封闭式评估的局限性。本研究结果倡导采用更稳健灵活、聚焦特定文化代理指标的评估框架,以推动对LLMs文化对齐能力开展更细致准确的评估。

英文摘要:

A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment 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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