Summary of Mobileagentbench: An Efficient and User-friendly Benchmark For Mobile Llm Agents, by Luyuan Wang et al.
MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents
by Luyuan Wang, Yongyu Deng, Yiwei Zha, Guodong Mao, Qinmin Wang, Tianchen Min, Wei Chen, Shoufa Chen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 proposes an efficient and user-friendly benchmark, MobileAgentBench, to evaluate the performance of large language model-based mobile agents. The authors identify a gap in existing research, which has not thoroughly compared the capabilities of various mobile agents. To address this challenge, they define 100 tasks across 10 open-source apps, categorized by multiple levels of difficulty. The paper evaluates several existing mobile agents, including AppAgent and MobileAgent, to systematically compare their performance. By providing a standardized benchmark, the authors aim to facilitate research and development in both academic and industrial sectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a way to test how well special kinds of computer programs can work with your smartphone. These programs are called mobile agents, and they’re designed to help you manage tasks on your phone. Right now, it’s hard to compare the different mobile agents because there’s no standard way to test them. The authors created a new benchmark that defines 100 tasks across 10 different apps, so researchers can see which mobile agents are best at completing those tasks. |
Keywords
» Artificial intelligence » Large language model
