A new 1,248-instance paired benchmark shows 18 vision-language models drop substantially in accuracy when images contain misleading visual cues.
Lvlm-interpret: an interpretability tool for large vision-language models
3 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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representative citing papers
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
Enhanced EWC for LVLMs cuts forgetting rates by 78% versus naive training and keeps visual-textual alignment with 15% extra compute.
citing papers explorer
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MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
A new 1,248-instance paired benchmark shows 18 vision-language models drop substantially in accuracy when images contain misleading visual cues.
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The Case for Model Science: Verify, Explore, Steer, Refine
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
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Lifelong Learning in Vision-Language Models: Enhanced EWC with Cross-Modal Knowledge Retention
Enhanced EWC for LVLMs cuts forgetting rates by 78% versus naive training and keeps visual-textual alignment with 15% extra compute.