hallucination
AutomatedMulti-levelPreferenceforMLLMs
However, Is asingle comparison between superior and inferior responses sufficient for preference learning in MLLMs? Upon consideration, we find that a multi-level preference framework offers greater benefits for preference learning, primarily due to two main intuitive advantages.
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Understanding Hallucinations in Diffusion Models through Mode Interpolation
But where do such hallucinations come from?
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CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Models Junho Kim Hyun Jun Kim Y eon Ju Kim Yong Man Ro
Large Multi-modal Models (LMMs) have recently demonstrated remarkable abilities in visual context understanding and coherent response generation.
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Alleviating Hallucinations in Large Vision-Language Models through Hallucination-Induced Optimization
To tackle this issue, we conducted the theoretical analysis to promote the effectiveness of contrast decoding.
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.47)
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- Europe > Switzerland > Zürich > Zürich (0.14)
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- Research Report > Experimental Study (1.00)
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