Customized large language models can outperform Community Notes in correcting misinformation

Understand

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.