Summary of Mllm-as-a-judge: Assessing Multimodal Llm-as-a-judge with Vision-language Benchmark, by Dongping Chen et al.
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, Yao Wan, Pan Zhou, Lichao Sun
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel benchmark, MLLM-as-a-Judge, to assess the ability of Multimodal Large Language Models (MLLMs) in assisting judges across diverse modalities. The benchmark evaluates MLLMs’ performance in three tasks: Scoring Evaluation, Pair Comparison, and Batch Ranking. While MLLMs demonstrate remarkable human-like discernment in Pair Comparison, they diverge significantly from human preferences in Scoring Evaluation and Batch Ranking. The study reveals persistent challenges in the judgment capacities of LLMs, including diverse biases, hallucinatory responses, and inconsistencies in judgment, even in advanced models like GPT-4V. This emphasizes the need for enhancements and further research to make MLLMs fully reliable evaluators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special test for big language models that can understand many types of information, like text, images, and sounds. It wants to see how well these models do when they’re helping judges make decisions. The test has three parts: scoring things, comparing two things, and ranking a group of things. The results show that the models are really good at one part but not so good at the others. They also have some big problems with making fair judgments, even in the best models. This means we need to make them better before we can trust them to help judges. |
Keywords
» Artificial intelligence » Gpt
