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    <abstract>The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents for better data privacy, we introduce DroidCall, the first training and testing dataset for accurate Android Intent invocation. With a highly flexible and reusable data generation pipeline, we constructed 10k samples in DroidCall. Given a task instruction in natural language, small language models such as Qwen2.5-3B and Gemma2-2B fine-tuned with DroidCall can approach or even surpass the capabilities of GPT-4o for accurate Android intent invocation. We also provide an end-to-end Android app equipped with these fine-tuned models to demonstrate the Android intent invocation process. The code and dataset are available at https://github.com/UbiquitousLearning/DroidCall</abstract>
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%0 Conference Proceedings
%T DroidCall: A Dataset for LLM-powered Android Intent Invocation
%A Xie, Weikai
%A Zhang, Li
%A Wang, Shihe
%A Yi, Rongjie
%A Xu, Mengwei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F xie-etal-2025-droidcall
%X The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents for better data privacy, we introduce DroidCall, the first training and testing dataset for accurate Android Intent invocation. With a highly flexible and reusable data generation pipeline, we constructed 10k samples in DroidCall. Given a task instruction in natural language, small language models such as Qwen2.5-3B and Gemma2-2B fine-tuned with DroidCall can approach or even surpass the capabilities of GPT-4o for accurate Android intent invocation. We also provide an end-to-end Android app equipped with these fine-tuned models to demonstrate the Android intent invocation process. The code and dataset are available at https://github.com/UbiquitousLearning/DroidCall
%R 10.18653/v1/2025.findings-emnlp.484
%U https://aclanthology.org/2025.findings-emnlp.484/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.484
%P 9116-9134

Markdown (Informal)

[DroidCall: A Dataset for LLM-powered Android Intent Invocation](https://aclanthology.org/2025.findings-emnlp.484/) (Xie et al., Findings 2025)

• DroidCall: A Dataset for LLM-powered Android Intent Invocation (Xie et al., Findings 2025)

ACL

• Weikai Xie, Li Zhang, Shihe Wang, Rongjie Yi, and Mengwei Xu. 2025. DroidCall: A Dataset for LLM-powered Android Intent Invocation. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 9116–9134, Suzhou, China. Association for Computational Linguistics.