大语言模型与文化价值观：提示语言和显式文化框架的影响
LLMs and Cultural Values: the Impact of Prompt Language and Explicit Cultural Framing
AI总结：
本文通过11种语言和显式文化框架提示10个LLMs，发现提示语言与文化视角虽能改变输出，但模型仍系统性偏向荷兰、德国、美国和日本等少数国家的价值观，无法充分代表文化多样性。
摘要：
大语言模型（LLMs）正迅速被全球用户采用，用户以多种语言与其交互。与此同时，该技术的训练数据和优化目标存在有据可查的不平衡，这引发了人们对LLMs能否代表其广泛用户群文化多样性的质疑。在本研究中，我们关注LLMs与文化价值观，考察提示语言和文化框架如何影响模型响应及其与不同国家人类价值观的一致性。我们使用来自Hofstede价值观调查模块和世界价值观调查的63个题项对10个LLMs进行探测，这些题项被翻译成11种语言，并分别以带和不带不同显式文化视角的方式构造成提示。我们的研究证实，提示语言和文化视角都会导致LLM输出产生变化，但有一个重要警示：虽然有针对性的提示在一定程度上能够将LLM响应引导至相应国家的主导价值观方向，但它并未克服模型对数据集中一组受限国家相关价值观的系统性偏向，这些国家包括荷兰、德国、美国和日本。所有测试模型，无论其来源如何，都表现出极为相似的模式：它们在大多数话题上产生相当中立的响应，并在社会宽容等问题上采取选择性的进步立场。与人类受访者文化价值观的一致性通过显式文化视角比通过有针对性的提示语言得到更大改善。出乎意料的是，将两种方法结合并不比使用英语提示的文化框架更有效。这些发现揭示LLMs处于一个令人不安的中间地带：它们对提示变化有足够的响应性以产生变化，但又过于牢固地锚定在特定文化默认值上，无法充分代表文化多样性。
英文摘要：
Large Language Models (LLMs) are rapidly being adopted by users across the globe, who interact with them in a diverse range of languages. At the same time, there are well-documented imbalances in the training data and optimisation objectives of this technology, raising doubts as to whether LLMs can represent the cultural diversity of their broad user base. In this study, we look at LLMs and cultural values and examine how prompt language and cultural framing influence model responses and their alignment with human values in different countries. We probe 10 LLMs with 63 items from the Hofstede Values Survey Module and World Values Survey, translated into 11 languages, and formulated as prompts with and without different explicit cultural perspectives. Our study confirms that both prompt language and cultural perspective produce variation in LLM outputs, but with an important caveat: While targeted prompting can, to a certain extent, steer LLM responses in the direction of the predominant values of the corresponding countries, it does not overcome the models' systematic bias toward the values associated with a restricted set of countries in our dataset: the Netherlands, Germany, the US, and Japan. All tested models, regardless of their origin, exhibit remarkably similar patterns: They produce fairly neutral responses on most topics, with selective progressive stances on issues such as social tolerance. Alignment with cultural values of human respondents is improved more with an explicit cultural perspective than with a targeted prompt language. Unexpectedly, combining both approaches is no more effective than cultural framing with an English prompt. These findings reveal that LLMs occupy an uncomfortable middle ground: They are responsive enough to changes in prompts to produce variation, but too firmly anchored to specific cultural defaults to adequately represent cultural diversity.
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。
