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

arXiv:2403.13840v1 Announce Type: new
Abstract: 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 …

abstract adoption application arxiv cs.ai cs.cl cs.si information language language models large language large language models llms political retrieval summarization tasks text text generation type