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arXiv · 2511.19199

CLASH: A Benchmark for Cross-Modal Contradiction Detection

Teodora Popordanoska·Jiameng Li·Matthew B. Blaschko
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Abstract

Contradictory multimodal inputs are common in real-world settings, yet existing benchmarks typically assume input consistency and fail to evaluate cross-modal contradiction detection - a fundamental capability for preventing hallucinations and ensuring reliability. We introduce CLASH, a novel benchmark for multimodal contradiction detection, featuring COCO images paired with contradictory captions containing controlled object-level or attribute-level contradictions. The samples include targeted questions evaluated in both multiple-choice and open-ended formats. The benchmark provides an extensive fine-tuning set filtered through automated quality checks, alongside a smaller human-verified diagnostic set. Our analysis of state-of-the-art models reveals substantial limitations in recognizing cross-modal conflicts, exposing systematic modality biases and category-specific weaknesses. Furthermore, we empirically demonstrate that targeted fine-tuning on CLASH substantially enhances conflict detection capabilities.

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Teodora Popordanoska, Jiameng Li, Matthew B. Blaschko. 2025-11-24. CLASH: A Benchmark for Cross-Modal Contradiction Detection. https://arxiv.org/abs/2511.19199

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