Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?

• URL: http://arxiv.org/abs/2411.05775v1
• Date: Fri, 08 Nov 2024 18:36:33 GMT
• Title: Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?
• Authors: Veronica Chatrath, Marcelo Lotif, Shaina Raza,
• Abstract summary: Political misinformation poses challenges to democratic processes, shaping public opinion and trust in media. This study investigates the use of state-of-the-art large language models (LLMs) as reliable annotators for detecting political factuality in news articles.
• Score: 2.321323878201932
• License:
• Abstract: Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability and annotator bias, while machine learning models require large, costly labelled datasets. This study investigates the use of state-of-the-art large language models (LLMs) as reliable annotators for detecting political factuality in news articles. Using open-source LLMs, we create a politically diverse dataset, labelled for bias through LLM-generated annotations. These annotations are validated by human experts and further evaluated by LLM-based judges to assess the accuracy and reliability of the annotations. Our approach offers a scalable and robust alternative to traditional fact-checking, enhancing transparency and public trust in media.

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