OMGuard combines interpretation-aware fine-tuning and rationale-guided headline rewriting to detect and correct omission-based misleadingness in multimodal news previews, raising an 8B model's performance to match a 235B LVLM.
Peng Qi, Zehong Yan, Wynne Hsu, and Mong Li Lee
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
EVian decomposes vision-language model responses into three cognitive components and audits them along consistency, coherence, and accuracy axes, showing that a small curated subset outperforms much larger training sets.
VeriEvol decouples prompt difficulty evolution from answer reliability verification to scale verified data for visual math reasoning, lifting benchmark accuracy from 35.42 to 54.73 and adding +3.88 in GRPO RL.
citing papers explorer
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What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews
OMGuard combines interpretation-aware fine-tuning and rationale-guided headline rewriting to detect and correct omission-based misleadingness in multimodal news previews, raising an 8B model's performance to match a 235B LVLM.
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Evian: Towards Explainable Visual Instruction-tuning Data Auditing
EVian decomposes vision-language model responses into three cognitive components and audits them along consistency, coherence, and accuracy axes, showing that a small curated subset outperforms much larger training sets.
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VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
VeriEvol decouples prompt difficulty evolution from answer reliability verification to scale verified data for visual math reasoning, lifting benchmark accuracy from 35.42 to 54.73 and adding +3.88 in GRPO RL.