LLM 在不同文化間如何準確代表價值觀？基於霍夫斯泰德文化維度的 LLM 回應的實證分析。

How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions

June 21, 2024

作者: Julia Kharchenko, Tanya Roosta, Aman Chadha, Chirag Shah

cs.AI

摘要

大型語言模型（LLMs）試圖透過以一種取悅人的方式回應人類來模仿人類行為，包括遵循他們的價值觀。然而，人類來自具有不同價值觀的多元文化。了解LLMs是否基於用戶所知國家的刻板價值觀向用戶展示不同價值觀至關重要。我們通過基於5個霍夫斯泰德文化維度的建議請求系列提示不同的LLMs，這是一種量化表示國家價值觀的方式。在每個提示中，我們融入代表36個不同國家的人物角色，以及與每個國家緊密聯繫的語言，以分析LLMs對文化理解的一致性。通過我們對回應的分析，我們發現LLMs能夠區分價值觀的一面和另一面，並理解不同國家有不同的價值觀，但在給出建議時並不總是堅持這些價值觀，也未能理解根據不同文化價值觀作出不同回答的必要性。基於這些發現，我們提出了培訓價值對齊和具有文化敏感性的LLMs的建議。更重要的是，這裡開發的方法和框架可以幫助進一步了解和減輕LLMs與文化和語言對齊問題。

English

Large Language Models (LLMs) attempt to imitate human behavior by responding to humans in a way that pleases them, including by adhering to their values. However, humans come from diverse cultures with different values. It is critical to understand whether LLMs showcase different values to the user based on the stereotypical values of a user's known country. We prompt different LLMs with a series of advice requests based on 5 Hofstede Cultural Dimensions -- a quantifiable way of representing the values of a country. Throughout each prompt, we incorporate personas representing 36 different countries and, separately, languages predominantly tied to each country to analyze the consistency in the LLMs' cultural understanding. Through our analysis of the responses, we found that LLMs can differentiate between one side of a value and another, as well as understand that countries have differing values, but will not always uphold the values when giving advice, and fail to understand the need to answer differently based on different cultural values. Rooted in these findings, we present recommendations for training value-aligned and culturally sensitive LLMs. More importantly, the methodology and the framework developed here can help further understand and mitigate culture and language alignment issues with LLMs.