Whose Side Are You On? Investigating the Political Stance of Large Language Models

Understand

Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval.

• As the widespread adoption of LLMs continues to surge, it becomes increasingly crucial to ensure that these models yield responses that are politically impartial, with the aim of preventing information bubbles, upholding fairness in representation, and mitigating confirmation bias.
• In this paper, we propose a quantitative framework and pipeline designed to systematically investigate the political orientation of LLMs.
• Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues.