Summary of A Survey on Multimodal Benchmarks: in the Era Of Large Ai Models, by Lin Li and Guikun Chen and Hanrong Shi and Jun Xiao and Long Chen
A Survey on Multimodal Benchmarks: In the Era of Large AI Models
by Lin Li, Guikun Chen, Hanrong Shi, Jun Xiao, Long Chen
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multimedia (cs.MM)
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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 The paper surveys 211 benchmarks used to evaluate Multimodal Large Language Models (MLLMs) across four core domains: understanding, reasoning, generation, and application. MLLMs have made significant strides in artificial intelligence, enabling the creation and comprehension of multimodal content. This survey provides a thorough analysis of task designs, evaluation metrics, and dataset constructions for each benchmark. The authors identify promising directions for future work and hope that this study will contribute to the ongoing advancement of MLLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at many ways we test large language models that can understand and create different types of content, like images, music, and text. These models are getting better at doing lots of things, from understanding what we say to generating new ideas. The researchers looked at 211 ways we test these models and found patterns in how they’re designed and tested. They want this study to help make the models even better by showing what works well and what doesn’t. |
