Customized large language models can outperform Community Notes in correcting misinformation

Abstract

Addressing misinformation in real-world settings is challenging: content is often multimodal; factuality judgments are nuanced and context-dependent; new events emerge rapidly across domains; corrections must be timely, trustworthy, and politically impartial; and multidimensional, multistakeholder frameworks remain lacking. Crowdsourced fact-checking systems such as Community Notes have gained broad adoption, but timely, scalable coverage remains difficult. We introduce MUSE, which augments large language models (LLMs) with trust-aware retrieval of up-to-date evidence and task-specific multimodal reasoning. Given a piece of content, MUSE identifies whether and which parts may be false or misleading and provides explanations grounded in credible references. We also develop an evaluation framework that assesses expert-rated response quality---including identification accuracy, explanation factuality, and the relevance and credibility of supporting references---as well as user perceptions. Across social media posts spanning modalities, domains, political leanings, misinformation tactics, and popularity, MUSE consistently produces high-quality responses, including for content not previously fact-checked online, and outperforms even highly rated Community Notes by 29%. It also improves participants' recognition of misinformation by 10%. Our work establishes a general methodological and evaluative framework for timely, scalable, and trustworthy correction of misinformation.