Unified Hallucination Fuzzing for Multimodal Large Language Models
Uncovering the fragile boundaries of multimodal intelligence through self-adaptive stress testing.
Hallucination remains a persistent challenge for Multimodal Large Language Models (MLLMs), severely limiting their reliability in high-stakes applications. Existing evaluations, predominantly based on static benchmarks, suffer from narrow taxonomical coverage and rapid performanc...
ArXiv CS.CL
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This research highlights the critical gap between static benchmark scores and actual model reliability, revealing that current MLLMs often mask fundamental errors behind surface-level proficiency. By integrating self-evolving stress testing, the study exposes deep-seated flaws in how models handle hallucinations, forcing a re-evaluation of alignment strategies.
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