Claim2Vec is a novel multilingual embedding model designed to represent fact-check claims as vectors for improved semantic understanding. It addresses the challenge of claim clustering for misinformation by leveraging contrastive learning on similar multilingual claim pairs, significantly enhancing performance.
An AI misinformation expert professor was fooled by hallucinated ChatGPT citations, highlighting the current challenge of verifying AI-generated content. Unlike traditional fact-checking, AI content lacks traceable sources, authorship, and editorial standards.
Knowledge workers spend 4.3 hours weekly fact-checking AI outputs, with the most dangerous failure mode being "plausible-neighbor substitution" rather than hallucinations. This mode provides statistically close but incorrect answers that often pass casual inspection, proving more problematic than obvious errors.
This work introduces the first benchmark for multimodal claim extraction from social media posts, essential for automated fact-checking. It evaluates state-of-the-art MLLMs and proposes MICE, an intent-aware framework, to address challenges in modeling rhetorical intent and contextual cues.
This article describes the building of a multi-LLM debate engine that fact-checks itself in real time to combat LLMs' tendency towards sycophancy and hallucination. It proposes a structured debate between agents with distinct roles, including a dedicated fact-checker agent mid-debate.