AIBullisharXiv – CS AI · 4h ago7/10
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Decoding by Perturbation: Mitigating MLLM Hallucinations via Dynamic Textual Perturbation
Researchers introduce Decoding by Perturbation (DeP), a training-free method that reduces hallucinations in multimodal large language models by applying controlled textual perturbations during decoding. The approach addresses the core issue where language priors override visual evidence, achieving improvements across multiple benchmarks without requiring model retraining or visual manipulation.