MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM
Journal:
arXiv
Published Date:
May 30, 2025
Abstract
Multimodal hallucination in multimodal large language models (MLLMs)
restricts the correctness of MLLMs. However, multimodal hallucinations are
multi-sourced and arise from diverse causes. Existing benchmarks fail to
adequately distinguish between perception-induced hallucinations and
reasoning-induced hallucinations. This failure constitutes a significant issue
and hinders the diagnosis of multimodal reasoning failures within MLLMs. To
address this, we propose the {\dataset} benchmark, which isolates reasoning
hallucinations by constructing questions where input images are correctly
perceived by MLLMs yet reasoning errors persist. {\dataset} introduces
multi-granular evaluation metrics: accuracy, factuality, and LLMs hallucination
score for hallucination quantification. Our analysis reveals that (1) the model
scale, data scale, and training stages significantly affect the degree of
logical, fabrication, and factual hallucinations; (2) current MLLMs show no
effective improvement on spatial hallucinations caused by misinterpreted
spatial relationships, indicating their limited visual reasoning capabilities;
and (3) question types correlate with distinct hallucination patterns,
highlighting targeted challenges and potential mitigation strategies. To
address these challenges, we propose {\method}, a method that combines
curriculum reinforcement fine-tuning to encourage models to generate
logic-consistent reasoning chains by stepwise reducing learning difficulty, and
collaborative hint inference to reduce reasoning complexity. {\method}
establishes a baseline on {\dataset}, and reduces the logical hallucinations in
original base models.
