从词语到世界：通过词语联想测试评估并缓解大语言模型中的文化偏见
From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test
AI总结：
本研究将词语联想测试扩展为适配LLM的任务以评估其文化偏见，提出CultureSteer方法将文化语义关联嵌入模型内部空间，有效提升跨文化认知对齐，为构建更具包容性的语言技术提供新范式。
摘要：
以人为中心的词语联想测试（WAT）是一种认知代理，通过文化共享的语义预期和由生活经验塑造的内隐语言模式揭示社会文化差异。我们将该测试扩展为适配大语言模型（LLM）的自由关联任务，以评估大语言模型与跨文化认知的对齐程度。为解决文化偏好问题，我们提出CultureSteer这一创新方法，它超越了表面的文化提示，将特定文化的语义关联直接嵌入模型的内部表征空间。实验表明，当前的大语言模型在词语联想层面存在对西方（尤其是美国）认知图式的显著偏见。相比之下，我们的模型大幅提升了跨文化对齐能力，能够捕捉多样化的语义关联。在文化敏感的下游任务上的进一步验证证实了其在促进跨文化认知对齐方面的有效性。本工作为增强大语言模型的文化意识贡献了一种新颖的方法范式，推动了更具包容性的语言技术的发展。
英文摘要：
The human-centered word association test (WAT) serves as a cognitive proxy, revealing sociocultural variations through culturally shared semantic expectations and implicit linguistic patterns shaped by lived experiences. We extend this test into an LLM-adaptive, free-relation task to assess the alignment of large language models (LLMs) with cross-cultural cognition. To address culture preference, we propose CultureSteer, an innovative approach that moves beyond superficial cultural prompting by embedding cultural-specific semantic associations directly within the model's internal representation space. Experiments show that current LLMs exhibit significant bias toward Western (notably American) schemas at the word association level. In contrast, our model substantially improves cross-cultural alignment, capturing diverse semantic associations. Further validation on culture-sensitive downstream tasks confirms its efficacy in fostering cognitive alignment across cultures. This work contributes a novel methodological paradigm for enhancing cultural awareness in LLMs, advancing the development of more inclusive language technologies.
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。
