Year: 2025
Type: conference-paper
Abstract: Large language models have opened up a world of possibilities for various NLP tasks, sparking optimism for the future.Despite their potential, LLMs have yet to be widely used as agents on real mobile devices.The main challenge is the need for high-quality data sources.Time constraints and labor intensity often hinder human annotation.On the other hand, existing LLMs exhibit inadequate completion rates and need a robust data filtration strategy.Given these challenges, we develop a framework called ANDROIDGEN to enhance the capabilities of LLM-based agents under data scarcity.In addition, we leverage AN-DROIDGEN to collect trajectories given human tasks and train open-source LLMs on these trajectories to develop an open-source mobile agent without manually labeled trajectories.We extensively evaluate ANDROIDGEN with AndroidWorld, AitW, and various popular applications, demonstrating its improvements and revealing potential areas for future improvement.Code, model, and data are available at https://github.com/THUDM/AndroidGen.
Authors: Hanyu Lai, Junjie Gao, Xiao Liu, Yifan Xu, Shudan Zhang, Yuxiao Dong, Jie Tang
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886774