Correcting misinformation on social media with a large language model
arxiv(2024)
摘要
Misinformation undermines public trust in science and democracy, particularly
on social media where inaccuracies can spread rapidly. Experts and laypeople
have shown to be effective in correcting misinformation by manually identifying
and explaining inaccuracies. Nevertheless, this approach is difficult to scale,
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 plausible but false content and references,
and limitations in addressing multimodal information. To address these issues,
we propose MUSE, an LLM augmented with access to and credibility evaluation of
up-to-date information. By retrieving contextual evidence and refutations, MUSE
can provide accurate and trustworthy explanations and references. It also
describes visuals and conducts multimodal searches for correcting multimodal
misinformation. We recruit fact-checking and journalism experts to evaluate
corrections to real social media posts across 13 dimensions, ranging from the
factuality of explanation to the relevance of references. The results
demonstrate MUSE's ability to correct misinformation promptly after appearing
on social media; overall, MUSE outperforms GPT-4 by 37
corrections from laypeople by 29
to combat real-world misinformation effectively and efficiently.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要