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arXiv Computation and Language By Yun-Ang Wu, Xanh Ho, Andre Greiner-Petter, Sunisth Kumar, Tian Cheng Xia, Florian Boudin, Akiko Aizawa

How Robust Is Multimodal Claim Verification to LLM Rewriting?

Read the original on arXiv Computation and Language →

The paper investigates how stylistic changes introduced by large language models (LLMs) affect multimodal claim verification, a task that determines whether a textual claim is supported by given evidence. Two rewriting strategies are used: natural rewriting, mimicking typical academic polishing, and controlled injection, adding a single LLM-associated word. Across 11 open‑weight models (2B–38B parameters) from five VLM families, the study finds that most models remain robust to these modifications, showing no significant accuracy drop, though consistent probability shifts—especially under hedging conditions—are observed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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