Summary of Reference-free Hallucination Detection For Large Vision-language Models, by Qing Li et al.
Reference-free Hallucination Detection for Large Vision-Language Models
by Qing Li, Jiahui Geng, Chenyang Lyu, Derui Zhu, Maxim Panov, Fakhri Karray
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 abstract presents a study on evaluating hallucinations in large vision-language models (LVLMs) without relying on external tools. The authors investigate the effectiveness of three reference-free methods – uncertainty-based, consistency-based, and supervised uncertainty quantification – on four representative LVLMs across two tasks. They find that these approaches can efficiently detect non-factual responses in LVLMs, with the supervised uncertainty quantification method achieving the best performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large vision-language models are super smart computers that can understand and answer questions about pictures. They’re great at chatting with us too! But sometimes they make things up that aren’t true. The researchers looked into ways to figure out when these “hallucinations” happen without needing special tools. They tested three different methods on four of these super smart computers, doing two kinds of tasks. It turns out that these methods can actually help us catch when the computer is making something up! |
Keywords
» Artificial intelligence » Supervised
