Large Language Models have been widely been adopted by users for writing tasks such as sentence completions. While this can improve writing efficiency, prior research shows that LLM-generated suggestions may exhibit cultural biases which may be difficult for users to detect, especially in educational contexts for non-native English speakers. While such prior work has studied the biases in LLM moral value alignment, we aim to investigate cultural biases in LLM recommendations for real-world entities. To do so, we use the WEIRD (Western, Educated, Industrialized, Rich and Democratic) framework to evaluate recommendations by various LLMs across a dataset of fine-grained entities, and apply pluralistic prompt-based strategies to mitigate these biases. Our results indicate that while such prompting strategies do reduce such biases, this reduction is not consistent across different models, and recommendations for some types of entities are more biased than others.

翻译：大型语言模型已被用户广泛采纳用于句子补全等写作任务。虽然这能提高写作效率，但先前研究表明，LLM生成的建议可能表现出文化偏见，这些偏见可能难以被用户察觉，尤其是在非英语母语者的教育情境中。尽管已有研究探讨了LLM道德价值对齐中的偏见，我们旨在调查LLM对现实世界实体推荐中的文化偏见。为此，我们采用WEIRD（西方、受过教育、工业化、富裕和民主）框架评估多种LLM在细粒度实体数据集上的推荐，并应用基于多元提示的策略来缓解这些偏见。我们的结果表明，虽然此类提示策略确实减少了偏见，但这种减少在不同模型间并不一致，且对某些类型实体的推荐比其他类型存在更明显的偏见。