大型语言模型反映了其创造者的意识形态
Large Language Models Reflect the Ideology of their Creators
AI总结：
本文通过让多语言LLM描述政治人物，发现不同地缘政治区域和语言的模型在道德评估上存在规范性差异，表明LLM的意识形态立场反映了其创造者的世界观。
摘要：
大型语言模型（LLM）在大量数据上进行训练以生成自然语言，使其能够执行文本摘要和问答等任务。这些模型在像ChatGPT这样的人工智能（AI）助手中变得流行，并且已经在人类获取信息的方式中扮演着有影响力的角色。然而，LLM的行为因其设计、训练和使用方式而异。在本文中，我们提示一个由流行LLM组成的多样化小组，用联合国的所有六种官方语言描述大量具有政治相关性的杰出人物。通过识别和分析其回答中反映的道德评估，我们发现了来自不同地缘政治区域的LLM之间，以及同一LLM在不同语言提示下回答之间的规范性差异。仅在美国的模型中，我们发现普遍假设的政治观点差异反映在与进步价值观相关的显著规范差异中。在中国模型中，我们刻画了面向国际和面向国内的模型之间的分歧。我们的结果表明，LLM的意识形态立场似乎反映了其创造者的世界观。这带来了政治工具化的风险，并引发了对旨在使LLM在意识形态上“无偏见”的技术和监管努力的担忧。
英文摘要：
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants like ChatGPT and already play an influential role in how humans access information. However, the behavior of LLMs varies depending on their design, training, and use. In this paper, we prompt a diverse panel of popular LLMs to describe a large number of prominent personalities with political relevance, in all six official languages of the United Nations. By identifying and analyzing moral assessments reflected in their responses, we find normative differences between LLMs from different geopolitical regions, as well as between the responses of the same LLM when prompted in different languages. Among only models in the United States, we find that popularly hypothesized disparities in political views are reflected in significant normative differences related to progressive values. Among Chinese models, we characterize a division between internationally- and domestically-focused models. Our results show that the ideological stance of an LLM appears to reflect the worldview of its creators. This poses the risk of political instrumentalization and raises concerns around technological and regulatory efforts with the stated aim of making LLMs ideologically 'unbiased'.
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。
