@inproceedings{lai-etal-2025-androidgen,
title = "{A}ndroid{G}en: Building an Android Language Agent under Data Scarcity",
author = "Lai, Hanyu and
Gao, Junjie and
Liu, Xiao and
Xu, Yifan and
Zhang, Shudan and
Dong, Yuxiao and
Tang, Jie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.138/",
doi = "10.18653/v1/2025.acl-long.138",
pages = "2727--2749",
ISBN = "979-8-89176-251-0",
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 AndroidGen 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."
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%0 Conference Proceedings
%T AndroidGen: Building an Android Language Agent under Data Scarcity
%A Lai, Hanyu
%A Gao, Junjie
%A Liu, Xiao
%A Xu, Yifan
%A Zhang, Shudan
%A Dong, Yuxiao
%A Tang, Jie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F lai-etal-2025-androidgen
%X 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 AndroidGen 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.
%R 10.18653/v1/2025.acl-long.138
%U https://aclanthology.org/2025.acl-long.138/
%U https://doi.org/10.18653/v1/2025.acl-long.138
%P 2727-2749
Markdown (Informal)
[AndroidGen: Building an Android Language Agent under Data Scarcity](https://aclanthology.org/2025.acl-long.138/) (Lai et al., ACL 2025)
ACL
- Hanyu Lai, Junjie Gao, Xiao Liu, Yifan Xu, Shudan Zhang, Yuxiao Dong, and Jie Tang. 2025. AndroidGen: Building an Android Language Agent under Data Scarcity. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2727–2749, Vienna, Austria. Association for Computational Linguistics.