LLM在跨文化价值观表达方面表现如何？基于霍夫斯泰德文化维度的LLM回应的实证分析。

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

大型语言模型（LLMs）试图通过以一种取悦用户的方式回应人类来模仿人类行为，包括遵循他们的价值观。然而，人类来自具有不同价值观的多元文化。重要的是要了解LLMs是否会根据用户所在国家的刻板价值观向用户展示不同的价值观。我们使用基于5个霍夫斯泰德文化维度的一系列建议请求来提示不同的LLMs，这是一种量化表示国家价值观的方式。在每个提示中，我们包含代表36个不同国家的人物角色，以及与每个国家主要相关的语言，以分析LLMs对文化理解的一致性。通过对回应的分析，我们发现LLMs能够区分价值观的一面和另一面，以及理解不同国家具有不同的价值观，但在给出建议时并不总是遵循这些价值观，并且未能理解根据不同文化价值观作出不同回答的必要性。基于这些发现，我们提出了培训价值观一致且具有文化敏感性的LLMs的建议。更重要的是，这里开发的方法和框架可以帮助进一步了解并缓解LLMs与文化和语言对齐问题。

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.