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.
It only shows isolated or local cases, or uses extreme examples in a way that underplays or exaggerates the true scale, prevalence, or impact described in the full news context
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.