ComPaSS estimates commonsense plausibility by quantifying semantic shifts induced by augmenting sentences with related information and outperforms generative baselines on fine-grained tasks for language and vision-language models.
IEEE/ACM Transactions on Audio, Speech, and Lan- guage Processing, 30:594–604
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AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.
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Estimating Commonsense Plausibility through Semantic Shifts
ComPaSS estimates commonsense plausibility by quantifying semantic shifts induced by augmenting sentences with related information and outperforms generative baselines on fine-grained tasks for language and vision-language models.
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Attention Grounded Enhancement for Visual Document Retrieval
AGREE boosts visual document retrieval by adding local relevance signals from MLLM attention maps to global document labels during retriever training.