Summary of Mme-survey: a Comprehensive Survey on Evaluation Of Multimodal Llms, by Chaoyou Fu et al.
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
by Chaoyou Fu, Yi-Fan Zhang, Shukang Yin, Bo Li, Xinyu Fang, Sirui Zhao, Haodong Duan, Xing Sun, Ziwei Liu, Liang Wang, Caifeng Shan, Ran He
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents a comprehensive survey on evaluating Multimodal Large Language Models (MLLMs). These models are designed to develop multimodal perception and reasoning capabilities, such as writing code from flowcharts or creating stories based on images. To improve these models, accurate evaluation is crucial, providing intuitive feedback and guidance. Unlike traditional train-eval-test paradigms focusing on single tasks like image classification, MLLMs require various new benchmarks and evaluation methods. The paper discusses four key aspects: summarized benchmark types divided by evaluation capabilities; the typical process of benchmark construction, including data collection, annotation, and precautions; a systematic evaluation manner consisting of judges, metrics, and toolkits; and an outlook for future benchmarks. This work aims to provide researchers with an easy grasp on effectively evaluating MLLMs according to different needs, inspiring better evaluation methods and driving progress in MLLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can test Multimodal Large Language Models (MLLMs) to make sure they’re working well. These models are special because they can do things like write code from flowcharts or tell stories based on images. To get these models better, we need to make sure we’re testing them correctly. Right now, there are different ways to test MLLMs, and the paper looks at four main parts: what types of tests we use, how we build those tests, how we judge how well the model is doing, and what might come next in terms of testing. The goal is to make it easier for researchers to figure out how to test these models correctly and get them better. |
Keywords
» Artificial intelligence » Image classification
