The Political Preferences of LLMs

We report here a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs). Namely, we administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs, both close and open source. The results indicate that when probed with questions/statements with political connotations most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. We note that this is not the case for base (i.e. foundation) models upon which LLMs optimized for conversation with humans are built. However, base models' suboptimal performance at coherently answering questions suggests caution when interpreting their classification by political orientation tests. Though not conclusive, our results provide preliminary evidence for the intriguing hypothesis that the embedding of political preferences into LLMs might be happening mostly post-pretraining. Namely, during the supervised fine-tuning (SFT) and/or Reinforcement Learning (RL) stages of the conversational LLMs training pipeline. We provide further support for this hypothesis by showing that LLMs are easily steerable into target locations of the political spectrum via SFT requiring only modest compute and custom data, illustrating the ability of SFT to imprint political preferences onto LLMs. As LLMs have started to displace more traditional information sources such as search engines or Wikipedia, the implications of political biases embedded in LLMs has important societal ramifications.