  Real-world misinformation can be partially correct and even factual but
misleading. It undermines public trust in science and democracy, particularly
on social media, where it can spread rapidly. High-quality and timely
correction of misinformation that identifies and explains its (in)accuracies
has been shown to effectively reduce false beliefs. Despite the wide acceptance
of manual correction, it is difficult to be timely and scalable, a concern as
technologies like large language models (LLMs) make misinformation easier to
produce. LLMs also have versatile capabilities that could accelerate
misinformation correction-however, they struggle due to a lack of recent
information, a tendency to produce false content, and limitations in addressing
multimodal information. We propose MUSE, an LLM augmented with access to and
credibility evaluation of up-to-date information. By retrieving evidence as
refutations or contexts, MUSE identifies and explains (in)accuracies in a piece
of content-not presupposed to be misinformation-with references. It also
describes images and conducts multimodal searches to verify and correct
multimodal content. Fact-checking experts evaluate responses to social media
content that are not presupposed to be (non-)misinformation but broadly include
incorrect, partially correct, and correct posts, that may or may not be
misleading. We propose and evaluate 13 dimensions of misinformation correction
quality, ranging from the accuracy of identifications and factuality of
explanations to the relevance and credibility of references. The results
demonstrate MUSE's ability to promptly write high-quality responses to
potential misinformation on social media-overall, MUSE outperforms GPT-4 by 37%
and even high-quality responses from laypeople by 29%. This work reveals LLMs'
potential to help combat real-world misinformation effectively and efficiently.
