MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866916283693400064 |
|---|---|
| author | Wang, Luyuan Deng, Yongyu Zha, Yiwei Mao, Guodong Wang, Qinmin Min, Tianchen Chen, Wei Chen, Shoufa |
| author_facet | Wang, Luyuan Deng, Yongyu Zha, Yiwei Mao, Guodong Wang, Qinmin Min, Tianchen Chen, Wei Chen, Shoufa |
| contents | Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) and their potential to autonomously manage daily tasks. Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents, due to the inexhaustible states of apps and the vague definition of feasible action sequences. To address this challenge, we propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing. We initially define 100 tasks across 10 open-source apps, categorized by multiple levels of difficulty. Subsequently, we evaluate several existing mobile agents, including AppAgent and MobileAgent, to thoroughly and systematically compare their performance. All materials are accessible on our project webpage: https://MobileAgentBench.github.io, contributing to the advancement of both academic and industrial fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents Wang, Luyuan Deng, Yongyu Zha, Yiwei Mao, Guodong Wang, Qinmin Min, Tianchen Chen, Wei Chen, Shoufa Artificial Intelligence Human-Computer Interaction Large language model (LLM)-based mobile agents are increasingly popular due to their capability to interact directly with mobile phone Graphic User Interfaces (GUIs) and their potential to autonomously manage daily tasks. Despite their promising prospects in both academic and industrial sectors, little research has focused on benchmarking the performance of existing mobile agents, due to the inexhaustible states of apps and the vague definition of feasible action sequences. To address this challenge, we propose an efficient and user-friendly benchmark, MobileAgentBench, designed to alleviate the burden of extensive manual testing. We initially define 100 tasks across 10 open-source apps, categorized by multiple levels of difficulty. Subsequently, we evaluate several existing mobile agents, including AppAgent and MobileAgent, to thoroughly and systematically compare their performance. All materials are accessible on our project webpage: https://MobileAgentBench.github.io, contributing to the advancement of both academic and industrial fields. |
| title | MobileAgentBench: An Efficient and User-Friendly Benchmark for Mobile LLM Agents |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.08184 |