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This paper introduces a method to automatically detect six different kinds of bias in news articles using large language models. Rather than relying on large hand-labeled datasets, the approach first adapts the model to news-style language and then uses "soft prompting" that needs only a small amount of labeled data. Tested on over 400,000 New York Times articles, it detects bias more accurately than standard prompting methods.
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Benjamin M. Ampel
Assistant Professor in Computer Information Systems and Director, Center for CyberAI Research (CCAIR)
My research focuses on AI-enabled Cybersecurity, including Cyber Threat Intelligence, Large Language Models, and Phishing Detection.