Evaluating Large Language Models on the Political Compass Test
A user on the r/LocalLLaMA subreddit shared and discussed the results of testing various commercial and local large language models (LLMs) on the Political Compass test. This evaluation aims to map the political alignment and potential biases of these models across economic and social dimensions. Understanding LLM bias is crucial as these models are increasingly integrated into decision-making systems and daily information retrieval. Evaluating their political alignment helps developers and users identify systemic biases and improve AI alignment strategies. The evaluation utilizes the Political Compass test, which maps political ideologies onto a two-dimensional grid consisting of an economic left-right axis and a social authoritarian-libertarian axis. The results compare how different open-source (local) and closed-source (commercial) models respond to politically sensitive prompts.
## BACKGROUND
The Political Compass is a popular online assessment tool that measures political beliefs beyond the traditional one-dimensional left-right spectrum. In the context of AI, alignment refers to the process of steering models to conform to human values and safety guidelines. Evaluating LLMs on such tests is a common method to detect whether training data or fine-tuning processes have introduced specific ideological biases.