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arXiv Computer Vision By Naeun Lee, Hyunjong Kim, Sunghwan Choi, Injin Kong, Yohan Jo

Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning

Read the original on arXiv Computer Vision →

The paper investigates whether Multimodal Large Language Models (MLLMs) can reason about visual persuasion. It finds that current models often shortcut by citing obvious visual elements instead of truly reasoning about diverse cues, and proposes a fine‑tuning method using multi‑perspective rationales along with an evaluation framework that separates prediction performance from rationale faithfulness. The study shows that while fine‑tuning improves prediction, higher accuracy does not guarantee more faithful reasoning, underscoring the need for distinct evaluation of effectiveness and faithfulness.

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 Computer Vision.

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