大型语言模型不谈论什么：一项关于审核与审查实践的实证研究
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
AI总结：
本文通过区分硬审查与软审查，对14个来自西方、中国和俄罗斯的LLM在联合国六种官方语言下的政治话题回应进行实证分析，发现审查普遍存在但主要面向提供者国内受众，呼吁提升LLM的多样性与审核透明度。
摘要：
大型语言模型（LLMs）越来越多地被部署为信息门户，但其内容审核实践仍未得到充分探索。本研究调查了LLMs在被问及政治话题时拒绝回答或省略信息的程度。为此，我们区分了硬审查（即生成的拒绝、错误消息或预设的否认回应）和软审查（即选择性省略或淡化关键要素），这些是在要求LLMs提供关于广泛政治人物信息时在其回应中识别出来的。我们的分析涵盖了来自西方国家、中国和俄罗斯的14个最先进模型，并以联合国（UN）全部六种官方语言进行提示。我们的分析表明，尽管审查在各模型中都存在，但它主要针对LLM提供者的国内受众而定制，并且通常表现为硬审查或软审查（但很少同时出现两者）。这些发现强调了在公开可用的LLMs中保持意识形态和地理多样性的必要性，以及提高LLM审核策略透明度的必要性，以促进用户做出知情选择。所有数据均可免费获取。
英文摘要：
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to which LLMs refuse to answer or omit information when prompted on political topics. To do so, we distinguish between hard censorship (i.e., generated refusals, error messages, or canned denial responses) and soft censorship (i.e., selective omission or downplaying of key elements), which we identify in LLMs' responses when asked to provide information on a broad range of political figures. Our analysis covers 14 state-of-the-art models from Western countries, China, and Russia, prompted in all six official United Nations (UN) languages. Our analysis suggests that although censorship is observed across the board, it is predominantly tailored to an LLM provider's domestic audience and typically manifests as either hard censorship or soft censorship (though rarely both concurrently). These findings underscore the need for ideological and geographic diversity among publicly available LLMs, and greater transparency in LLM moderation strategies to facilitate informed user choices. All data are made freely available.
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。
