Summary of Mmau: a Holistic Benchmark Of Agent Capabilities Across Diverse Domains, by Guoli Yin et al.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
by Guoli Yin, Haoping Bai, Shuang Ma, Feng Nan, Yanchao Sun, Zhaoyang Xu, Shen Ma, Jiarui Lu, Xiang Kong, Aonan Zhang, Dian Ang Yap, Yizhe zhang, Karsten Ahnert, Vik Kamath, Mathias Berglund, Dominic Walsh, Tobias Gindele, Juergen Wiest, Zhengfeng Lai, Xiaoming Wang, Jiulong Shan, Meng Cao, Ruoming Pang, Zirui Wang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 introduces the Massive Multitask Agent Understanding (MMAU) benchmark to comprehensively evaluate large language models (LLMs) as human-like agents. The MMAU benchmark features 20 meticulously designed tasks across five domains, assessing essential capabilities such as understanding, reasoning, planning, problem-solving, and self-correction. By testing 18 representative models on MMAU, the paper provides deep and insightful analyses of LLM strengths and limitations. This evaluation framework enhances the interpretability of LLM performance and sheds light on their capabilities and limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special test for big language models to see how well they can do tasks like tool-using, problem-solving, and math. It makes 20 small challenges that are different from each other, but all related to the same five skills: understanding, thinking logically, making plans, solving problems, and fixing mistakes. They tried 18 famous language models on these challenges and found out what they’re good at and what they struggle with. |
