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When an image and its caption disagree, can a user reliably control which one a vision-language model (VLM) follows? Accuracy alone cannot answer this question, since a model might give the correct answer from parametric knowledge without following the requested evidence. We therefore evaluate modality grounding contro...
MultiVENT-Raw is released, a multilingual collection of nearly 120,000 primarily raw videos paired with 130 events and 222 event-centric queries, along with human-annotated video relevance judgments and human-extracted key facts for relevant videos.
We propose LAYERSCOPE, a label-free, layerwise framework that aims to characterize a model's learned representations in video and multimodal settings. Evaluating downstream performance using representations from final or intermediate layers typically requires large amounts of labeled data, repeated task-specific evalua...
This work aims to explore how well vector-based pseudo relevance feedback can adapt to dense retrieval models when it is not feasible to use surface-form information to pick discriminating expansion tokens.
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