This research evaluates how large language models can identify claims that have already been fact-checked in other languages, reducing duplicate verification efforts.

• First comprehensive assessment of LLMs for multilingual claim matching
• Helps fact-checkers identify when false information crosses language barriers
• Demonstrates how AI can scale fact-checking efforts across linguistic boundaries
• Provides a security framework for combating global misinformation

This advancement matters for security professionals as it offers automated tools to detect recycled misinformation campaigns that exploit language differences to evade detection, strengthening information integrity across borders.

Large Language Models for Multilingual Previously Fact-Checked Claim Detection