Summary of Alleviating Hallucination in Large Vision-language Models with Active Retrieval Augmentation, by Xiaoye Qu et al.
Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentation
by Xiaoye Qu, Qiyuan Chen, Wei Wei, Jishuo Sun, Jianfeng Dong
First submitted to arxiv on: 1 Aug 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 The proposed Active Retrieval-Augmented large vision-language model (ARA) framework aims to address hallucinations in large vision-language models (LVLMs). By incorporating three critical dimensions: dissecting retrieval targets based on image hierarchical structures, pinpointing effective retrieval methods, and timing the process for low certainty episodes, ARA seeks to mitigate hallucinations. Empirical assessments across four benchmarks using three widely used LVLM models (LLaVA-1.5, Qwen-VL, and mPLUG-Owl2) demonstrate the effectiveness of utilizing fitting retrieval mechanisms and judiciously timing the retrieval process. This study provides insights on adapting retrieval augmentation to LVLMs for reducing hallucinations with minimal retrieval occurrences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models (LVLMs) are great at understanding images, but they often provide incorrect answers despite being plausible. Researchers have tried using external knowledge resources to improve these models, but this approach has limitations when applied to LVLMs. To address this issue, a new framework called Active Retrieval-Augmented large vision-language model (ARA) is proposed. ARA works by analyzing images in a hierarchical way, choosing the right methods for retrieving information, and timing its retrieval process carefully. This helps reduce hallucinations, which are incorrect answers that still look like they make sense. The study tested this approach using three different LVLM models and found it to be effective. |
Keywords
» Artificial intelligence » Language model
