Summary of Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in Lvlms, by Sreyan Ghosh and Chandra Kiran Reddy Evuru and Sonal Kumar and Utkarsh Tyagi and Oriol Nieto and Zeyu Jin and Dinesh Manocha
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
by Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar, Utkarsh Tyagi, Oriol Nieto, Zeyu Jin, Dinesh Manocha
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, known as hallucinations. The paper investigates the root causes of these hallucinations and finds that existing mitigation techniques primarily reduce them for visual recognition prompts but fail for cognitive prompts. The main issue is that LVLMs lack true visual perception, struggling to interpret images in context and link this recognition to their internal knowledge. To address this gap, the paper introduces Visual Description Grounded Decoding (VDGD), a simple method designed to enhance visual perception and improve reasoning capabilities. VDGD works by generating a detailed description of the image as a prefix to the instruction, favoring response tokens with lower KL divergence to the description. Experimental results on multiple visual reasoning benchmarks demonstrate that VDGD consistently outperforms existing baselines (2% – 33%). The paper also introduces VaLLu, a benchmark for comprehensive evaluation of LVLMs’ cognitive capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models sometimes make mistakes by saying things that aren’t true. Scientists want to understand why this happens and how to fix it. They found out that these models are good at recognizing what’s in pictures but struggle to understand the bigger picture and connect it to what they know. To help, they created a new way to make the models better at understanding pictures and making smart decisions. This method is called Visual Description Grounded Decoding. It works by first describing what’s in the picture and then using that description to help decide what to say. The scientists tested this method on several tasks and found that it worked much better than other methods (2% – 33%). They also created a new test, called VaLLu, to see how well these models can really understand things. |
