Summary of Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-language Models Via Targeted Instruction Tuning, by Rui Hu et al.
Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning
by Rui Hu, Yahan Tu, Jitao Sang
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework, DFTG, addresses the issue of hallucinations in large vision-language models (LVLMs) by generating targeted instruction data tailored to the specific hallucination patterns of each model. Building on recent work on high-quality instruction datasets, this paper highlights the importance of considering the hallucination specificity of different LVLMs when designing instruction data. By proposing a two-stage approach consisting of hallucination diagnosis and targeted data generation, the authors demonstrate that their method outperforms existing datasets in reducing hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models (LVLMs) have achieved great success on various tasks, but they still struggle with hallucinations, which can lead to inconsistencies between generated text and images. To address this issue, researchers have proposed high-quality instruction datasets, like LRV-Instruction, but these datasets didn’t consider the unique hallucination patterns of different LVLMs. This paper presents a new framework called DFTG that generates targeted instruction data based on the specific hallucinations of each model. The results show that this approach is more effective in reducing hallucinations than previous methods. |
Keywords
» Artificial intelligence » Hallucination
