Summary of Reducing Hallucinations in Vision-language Models Via Latent Space Steering, by Sheng Liu et al.
Reducing Hallucinations in Vision-Language Models via Latent Space Steering
by Sheng Liu, Haotian Ye, Lei Xing, James Zou
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 A novel approach is proposed to address hallucination in large vision-language models (LVLMs), a challenge hindering their deployment. Unlike language models, hallucination in LVLMs arises from misalignments between visual inputs and textual outputs. The paper investigates the mechanisms underlying this phenomenon, highlighting the unique structure of LVLMs that distinguishes them from large language models (LLMs). It is found that hallucinations often arise from the sensitivity of text decoders to vision inputs, a natural consequence of pre-training image encoders and text decoders separately. A novel technique called Visual and Textual Intervention (VTI) is introduced to reduce hallucinations by steering latent space representations during inference to enhance the stability of vision features. This task-agnostic test-time intervention can be easily applied to any problem without additional cost. Extensive experiments demonstrate that VTI effectively reduces hallucinations, outperforming baseline methods across multiple metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hallucination is a big problem for large vision-language models (LVLMs). These models are like super powerful computers that can understand both pictures and words, but sometimes they make mistakes by creating fake information. This happens because the parts of the model that look at pictures and the parts that understand words are trained separately. The authors of this paper figured out why this happens and came up with a new way to fix it. They call it Visual and Textual Intervention (VTI) and it helps the model be more careful when it’s looking at pictures. This means the model will be less likely to make mistakes and create fake information. The authors tested their method and found that it works really well. |
Keywords
» Artificial intelligence » Hallucination » Inference » Latent space
