Summary of Mitigating Hallucinations in Large Vision-language Models (lvlms) Via Language-contrastive Decoding (lcd), by Avshalom Manevich et al.
Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)
by Avshalom Manevich, Reut Tsarfaty
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This study introduces a novel algorithm called Language Contrastive Decoding (LCD) that reduces object hallucinations in Large Vision-Language Models (LVLMs). By leveraging the distribution confidence levels of Large Language Models (LLMs), LCD adjusts LVLM outputs to improve accuracy. The authors demonstrate the effectiveness of LCD on various benchmarks, including POPE F1 scores and CHAIR scores on the COCO validation set, achieving up to 4% improvement in POPE F1 scores and up to 36% reduction in CHAIR scores. Additionally, captioning quality scores are improved. This study highlights the potential of exploring LVLM-specific decoding algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision-Language Models can be super powerful tools for AI, but they have a problem: sometimes they make things up that aren’t really there! Researchers found a way to fix this by using an algorithm called Language Contrastive Decoding (LCD). LCD helps the models think more accurately by looking at what the Large Language Models are confident about. The results show that this works really well, making the models 4% better at one task and reducing mistakes by 36%. This is a big deal because it means we can use these powerful tools to do even more cool things with AI! |
