  Multimodal Large Language Models (MLLMs) have gained significant attention
recently, showing remarkable potential in artificial general intelligence.
However, assessing the utility of MLLMs presents considerable challenges,
primarily due to the absence of multimodal benchmarks that align with human
preferences. Drawing inspiration from the concept of LLM-as-a-Judge within
LLMs, this paper introduces a novel benchmark, termed MLLM-as-a-Judge, to
assess the ability of MLLMs in assisting judges across diverse modalities,
encompassing three distinct tasks: Scoring Evaluation, Pair Comparison, and
Batch Ranking. Our study reveals that, while MLLMs demonstrate remarkable
human-like discernment in Pair Comparison, there is a significant divergence
from human preferences in Scoring Evaluation and Batch Ranking. Furthermore, a
closer examination reveals persistent challenges in the judgment capacities of
LLMs, including diverse biases, hallucinatory responses, and inconsistencies in
judgment, even in advanced models such as GPT-4V. These findings emphasize the
pressing need for enhancements and further research efforts to be undertaken
before regarding MLLMs as fully reliable evaluators. In light of this, we
advocate for additional efforts dedicated to supporting the continuous
development within the domain of MLLM functioning as judges. The code and
dataset are publicly available at our project homepage:
\url{https://mllm-judge.github.io/}.
