MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
By Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Pan Zhou, Yao Wan, Lichao Sun. International Conference on Machine Learning, 2024
401 citations recorded by the source corpus.
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/}.
Subfields: Artificial Intelligence, Language and Linguistics
View Paper: https://www.semanticscholar.org/paper/1036f6dfe75af06fbcdb3447dbe9be8613bf857c
How researchers see this paper
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- Jiayi Ye
- Yanbo Wang
- Yue Huang
- Qihui Zhang
- Nuno Moniz
- Tian Gao
- Werner Geyer
- Chao Huang
Citations in the corpus
- Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge (2024, methodology): “We followed the experimental setup of Chen et al. (2024a) by setting the temperature to 0.7 and applied it to all judge models and generating models to ensure stable output quality and strong reproducibility.”