THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models

Prannay Kaul, Zhizhong Li, Hao Yang, Yonatan Dukler, Ashwin Swaminathan, C. J. Taylor, Stefano Soatto; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 27228-27238

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses which we term "Type I hallucinations". Instead they focus on hallucinations responding to very specific question formats---typically a multiple-choice response regarding a particular object or attribute---which we term "Type II hallucinations". Additionally such benchmarks often require external API calls to models which are subject to change. In practice we observe that a reduction in Type II hallucinations does not lead to a reduction in Type I hallucinations but rather that the two forms of hallucinations are often anti-correlated. To address this we propose THRONE a novel object-based automatic framework for quantitatively evaluating Type I hallucinations in LVLM free-form outputs. We use public language models (LMs) to identify hallucinations in LVLM responses and compute informative metrics. By evaluating a large selection of recent LVLMs using public datasets we show that an improvement in existing metrics do not lead to a reduction in Type I hallucinations and that established benchmarks for measuring Type I hallucinations are incomplete. Finally we provide a simple and effective data augmentation method to reduce Type I and Type II hallucinations as a strong baseline.