  Despite significant strides in multimodal tasks, Multimodal Large Language
Models (MLLMs) are plagued by the critical issue of hallucination. The reliable
detection of such hallucinations in MLLMs has, therefore, become a vital aspect
of model evaluation and the safeguarding of practical application deployment.
Prior research in this domain has been constrained by a narrow focus on
singular tasks, an inadequate range of hallucination categories addressed, and
a lack of detailed granularity. In response to these challenges, our work
expands the investigative horizons of hallucination detection. We present a
novel meta-evaluation benchmark, MHaluBench, meticulously crafted to facilitate
the evaluation of advancements in hallucination detection methods.
Additionally, we unveil a novel unified multimodal hallucination detection
framework, UNIHD, which leverages a suite of auxiliary tools to validate the
occurrence of hallucinations robustly. We demonstrate the effectiveness of
UNIHD through meticulous evaluation and comprehensive analysis. We also provide
strategic insights on the application of specific tools for addressing various
categories of hallucinations.
