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.
Cite
@article{arxiv.2412.00402,
title = {DroidCall: A Dataset for LLM-powered Android Intent Invocation},
author = {Weikai Xie and Li Zhang and Shihe Wang and Rongjie Yi and Mengwei Xu},
journal= {arXiv preprint arXiv:2412.00402},
year = {2024}
}