Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance
📰 ArXiv cs.AI
Residual Decoding mitigates hallucinations in Large Vision-Language Models by using history-aware residual guidance
Action Steps
- Identify hallucinations in Large Vision-Language Models as generated content that is coherent but irrelevant to visual input
- Propose Residual Decoding (ResDec) as a novel training method to address hallucinations
- Implement history-aware residual guidance in ResDec to improve model performance
- Evaluate the effectiveness of ResDec in reducing hallucinations and improving model accuracy
Who Needs to Know This
AI engineers and ML researchers working on vision-language models can benefit from this technique to improve model accuracy and reduce hallucinations, while data scientists can apply this method to various multimodal tasks
Key Insight
💡 Residual Decoding can mitigate hallucinations in Large Vision-Language Models by using history-aware residual guidance
Share This
💡 Reduce hallucinations in Vision-Language Models with Residual Decoding!
Key Takeaways
Residual Decoding mitigates hallucinations in Large Vision-Language Models by using history-aware residual guidance
Full Article
Title: Residual Decoding: Mitigating Hallucinations in Large Vision-Language Models via History-Aware Residual Guidance
Abstract:
arXiv:2602.01047v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training
Abstract:
arXiv:2602.01047v3 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) can reason from image-text inputs and perform well in various multimodal tasks. Despite this success, they are affected by language priors and often produce hallucinations. Hallucinations denote generated content that is grammatically and syntactically coherent, yet bears no match or direct relevance to visual input. To address this problem, we propose Residual Decoding (ResDec). It is a novel training
DeepCamp AI