Reference-free Hallucination Detection for Large Vision-Language Models
This work addresses the challenge of detecting hallucinations in LVLMs without relying on external references, which is important for improving model reliability in practical applications, though it is incremental as it builds on existing detection approaches.
The paper tackled the problem of hallucination detection in large vision-language models (LVLMs) by exploring reference-free methods, finding that supervised uncertainty quantification outperformed other techniques across different tasks and models.
Large vision-language models (LVLMs) have made significant progress in recent years. While LVLMs exhibit excellent ability in language understanding, question answering, and conversations of visual inputs, they are prone to producing hallucinations. While several methods are proposed to evaluate the hallucinations in LVLMs, most are reference-based and depend on external tools, which complicates their practical application. To assess the viability of alternative methods, it is critical to understand whether the reference-free approaches, which do not rely on any external tools, can efficiently detect hallucinations. Therefore, we initiate an exploratory study to demonstrate the effectiveness of different reference-free solutions in detecting hallucinations in LVLMs. In particular, we conduct an extensive study on three kinds of techniques: uncertainty-based, consistency-based, and supervised uncertainty quantification methods on four representative LVLMs across two different tasks. The empirical results show that the reference-free approaches are capable of effectively detecting non-factual responses in LVLMs, with the supervised uncertainty quantification method outperforming the others, achieving the best performance across different settings.