Papers by Assaf Ben-Kish

2 papers
Bridging the Visual Gap: Fine-Tuning Multimodal Models with Knowledge-Adapted Captions (2025.naacl-long)

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Challenge: Recent work focuses on training vision-language models with long, detailed image captions, but small-scale VLMs struggle to balance the richness of these captions with the risk of hallucinations.
Approach: They propose an evaluation framework that breaks down generated captions into individual propositions, assessing each in isolation.
Outcome: The proposed framework outperforms baselines in both automatic metrics and human evaluations on small-scale vision-language models with long, detailed captions.
Mitigating Open-Vocabulary Caption Hallucinations (2024.emnlp-main)

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Challenge: Existing methods for image captioning ignore the long-tailed nature of hallucinations . a new framework is proposed to address hallucines in image captions in the open-vocabulary setting .
Approach: They propose a framework to address hallucinations in image captioning in the open-vocabulary setting.
Outcome: The proposed framework surpasses the CHAIR benchmark in diversity and accuracy in open-vocabulary captioning.

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