跨语言共识：通过多语言自一致性对齐多语言文化知识
Cross-Lingual Consensus: Aligning Multilingual Cultural Knowledge via Multilingual Self-Consistency
AI总结：
提出一种自监督框架，利用多语言自一致性和自我批评机制，从本地语言表示中提取文化知识并迁移到英语，以缩小跨语言文化知识差距，在BLEnD基准上平均提升英语查询性能5.03%。
摘要：
尽管大型语言模型（LLMs）在各种任务中展现出强大的能力，但它们在不同语言之间表现出显著的性能差异。虽然用英语提示LLMs通常能获得最高的通用性能，但这往往会导致以西方为中心的偏见，阻碍模型准确反映多样化的文化知识。我们假设LLMs已经拥有嵌入在本地语言表示中的丰富文化知识，但在用英语提示时无法检索到这些知识。为了弥合这一跨语言知识差距，我们提出了一种新颖的自监督框架。我们的方法利用多语言自一致性来识别跨语言中最可靠的文化响应，并结合自我批评机制将这些知识转移到较弱的语言中。在BLEnD基准上的评估表明，我们的方法显著改善了文化对齐——在英语查询上平均提升5.03%——完全依赖于自生成数据。最终，我们的工作表明，潜在的文化知识可以成功地在语言之间浮现和传播，从而实现更具文化公平性和一致性的LLMs。
英文摘要：
Although Large Language Models (LLMs) demonstrate strong capabilities across various tasks, they exhibit significant performance discrepancies across languages. While prompting LLMs in English typically yields the highest general performance, it often induces a Western-centric bias, hindering the model's ability to accurately reflect diverse cultural knowledge. We hypothesize that LLMs already possess rich cultural knowledge embedded within local-language representations, but fail to retrieve it when prompted in English. To bridge this cross-lingual knowledge gap, we propose a novel self-supervised framework. Our method leverages multilingual self-consistency to identify the most reliable cultural responses across languages, combined with a self-critique mechanism to transfer this knowledge to the weaker language. Evaluations on the BLEnD benchmark demonstrate that our approach significantly improves cultural alignment-boosting performance on English queries by an average of 5.03%-relying entirely on self-generated data. Ultimately, our work demonstrates that latent cultural knowledge can be successfully surfaced and propagated across languages, enabling more culturally equitable and consistent 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。
