Reference-free Hallucination Detection for Large Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Large vision-language models exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, but they are prone to producing hallucinations. |
| Approach: | They propose to use supervised uncertainty quantification methods to detect hallucinations in large vision-language models. |
| Outcome: | The proposed methods outperform the others in detecting hallucinations on four representative LVLMs across two different tasks. |
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