Search Results for author:
Found 3 papers, 2 papers with code
The Mirage of Performance Gains: Why Contrastive Decoding Fails to Address Multimodal Hallucination
Contrastive decoding strategies are widely used to reduce hallucinations in multimodal large language models (MLLMs).
ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large language Models
However, these methods present two main limitations: (1) bluntly suppressing language priors can compromise coherence and accuracy of generated content, and (2) processing contrastive inputs adds computational load, significantly slowing inference speed.
Lifting the Veil on Visual Information Flow in MLLMs: Unlocking Pathways to Faster Inference
Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LLMs).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
