When Neutral Summaries are not that Neutral: Quantifying Political Neutrality in LLM-Generated News Summaries

• URL: http://arxiv.org/abs/2410.09978v1
• Date: Sun, 13 Oct 2024 19:44:39 GMT
• Title: When Neutral Summaries are not that Neutral: Quantifying Political Neutrality in LLM-Generated News Summaries
• Authors: Supriti Vijay, Aman Priyanshu, Ashique R. KhudaBukhsh,
• Abstract summary: This study presents a fresh perspective on quantifying the political neutrality of LLMs. We consider five pressing issues in current US politics: abortion, gun control/rights, healthcare, immigration, and LGBTQ+ rights. Our study reveals a consistent trend towards pro-Democratic biases in several well-known LLMs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In an era where societal narratives are increasingly shaped by algorithmic curation, investigating the political neutrality of LLMs is an important research question. This study presents a fresh perspective on quantifying the political neutrality of LLMs through the lens of abstractive text summarization of polarizing news articles. We consider five pressing issues in current US politics: abortion, gun control/rights, healthcare, immigration, and LGBTQ+ rights. Via a substantial corpus of 20,344 news articles, our study reveals a consistent trend towards pro-Democratic biases in several well-known LLMs, with gun control and healthcare exhibiting the most pronounced biases (max polarization differences of -9.49% and -6.14%, respectively). Further analysis uncovers a strong convergence in the vocabulary of the LLM outputs for these divisive topics (55% overlap for Democrat-leaning representations, 52% for Republican). Being months away from a US election of consequence, we consider our findings important.

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