Large Language Models (LLMs) are a transformational technology, fundamentally changing how people obtain information and interact with the world. As people become increasingly reliant on them for an enormous variety of tasks, a body of academic research has developed to examine these models for inherent biases, especially political biases, often finding them small. We challenge this prevailing wisdom. First, by comparing 31 LLMs to legislators, judges, and a nationally representative sample of U.S. voters, we show that LLMs' apparently small overall partisan preference is the net result of offsetting extreme views on specific topics, much like moderate voters. Second, in a randomized experiment, we show that LLMs can promulgate their preferences into political persuasiveness even in information-seeking contexts: voters randomized to discuss political issues with an LLM chatbot are as much as 5 percentage points more likely to express the same preferences as that chatbot. Contrary to expectations, these persuasive effects are not moderated by familiarity with LLMs, news consumption, or interest in politics. LLMs, especially those controlled by private companies or governments, may become a powerful and targeted vector for political influence.

本研究探讨了大型语言模型（LLMs）所存在的政治偏见，指出虽然普遍认为它们的偏见较小，但实际上是由于对特定议题极端观点的抵消所致。通过实验，我们发现LLMs在信息寻求环境中也能够导致政治上的说服性影响，使得与聊天机器人讨论政治问题的选民更可能表达与聊天机器人一致的偏好。这意味着LLMs可能成为一种强大的政治影响工具，尤其是由私企或政府控制的模型。

大型语言模型往往政治极端、意识形态不一致，即使在信息性环境中也具说服力