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arXiv · 2412.00402

DroidCall: A Dataset for LLM-powered Android Intent Invocation

Weikai Xie·Li Zhang·Shihe Wang·Rongjie Yi·Mengwei Xu
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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.

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Weikai Xie, Li Zhang, Shihe Wang, Rongjie Yi, Mengwei Xu. 2024-11-30. DroidCall: A Dataset for LLM-powered Android Intent Invocation. https://arxiv.org/abs/2412.00402

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