从回音室到认知单一文化：大型语言模型将时间偶然的党派立场呈现为知识

中文摘要 AI 辅助

大型语言模型（LLMs）正迅速成为公民与政治信息之间的界面。它们常被视为“更好的谷歌”。虽然这种类比在某些情况下可能成立，但对于民主政治而言，它却出乎意料地存在问题。搜索引擎检索由人类撰写的文档，而语言模型则生成新颖的文本，这些文本必然嵌入无形的框架决策。由于传达知识涉及框架，一个生成答案的系统无法充当通往“全人类知识”的中立渠道。相反，这些系统正成为一种新型的政治中介。机制性证据表明，党派身份在Llama 3.1 8B模型中被编码为可定位的几何方向，且对齐训练掩盖而非移除了这一结构。基于该证据，我们展示了利用模型2024年训练截止点的引导实验。这一截止点恰逢美国政治中由第二届特朗普政府和健康政治的MAHA转型所标志的戏剧性重组之前，为我们提供了自然实验。我们发现，该模型将时间偶然的党派立场呈现为知识，且没有区分事实与意见的机制。这一现实将信息环境从回音室推向认知单一文化，其中声称总结“全人类知识”的语言模型实际上只是放大了其训练数据中固有的文化和党派分歧。

英文摘要

Large language models are rapidly becoming an interface between citizens and political information. They are often regarded as "a better Google." While this analogy might work for some instances, it is unintuitively problematic for democratic politics. A search engine retrieves human-authored documents, while a language model generates novel text that necessarily embeds invisible framing decisions. Because conveying knowledge involves framing, a system that generates answers cannot serve as a neutral conduit to "all human knowledge." Instead, these systems are becoming a new kind of political intermediary. Mechanistic evidence shows that partisan identity is encoded as a locatable geometric direction inside the Llama 3.1 8B model, and that alignment training masks rather than removes this structure. Building on that evidence, we use the model's training cutoff in 2023 to make those frames visible. This cutpoint auspiciously falls just before a dramatic realignment in American politics marked by the second Trump administration and the MAHA transformation of health politics, providing us with a natural experiment. We find that the model presents temporally contingent partisan alignments as {\em knowledge}, with no reliable mechanism for distinguishing fact from opinion. Because citizens judge political claims by who made them and when, an answer that carries neither cue hampers the judgment on which self-governance depends. Large language models purporting to summarize "all human knowledge" are, in actuality, simply magnifying the cultural and partisan divides inherent in their training data.

发表机构

• Vanderbilt University（范德堡大学）
• University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）

机构由 AI 辅助整理，请以论文原文为准。