Summary of Mitigating Hallucinations in Large Vision-language Models with Instruction Contrastive Decoding, by Xintong Wang et al.
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding
by Xintong Wang, Jingheng Pan, Liang Ding, Chris Biemann
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The proposed Instruction Contrastive Decoding (ICD) method tackles a critical challenge in Large Vision-Language Models (LVLMs): reducing hallucinations during inference. ICD is designed to address the issue by increasing alignment uncertainty, effectively subtracting hallucinated concepts from the original distribution. The approach is inspired by the observation that disturbance instructions exacerbate hallucinations in multimodal fusion modules. Experimental results on discriminative and generative benchmarks demonstrate significant mitigation of object-level and attribute-level hallucinations, as well as enhanced general perception and recognition capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models are getting better at answering questions based on pictures, but they still make mistakes by adding extra information that isn’t in the picture. This paper introduces a new way to help these models be more accurate: Instruction Contrastive Decoding (ICD). The idea is simple: if we can identify what makes these models go wrong, we can make them better. The authors found that certain instructions or prompts make the models worse at understanding pictures, so they developed ICD to fix this problem. By using ICD, the models are much more accurate and can even recognize things in pictures better than before. |
Keywords
» Artificial intelligence » Alignment » Inference
