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DroidCall: A Dataset for LLM-powered Android Intent Invocation

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arxiv 2412.00402 v1 pith:WA2XPTWQ submitted 2024-11-30 cs.AI

Weikai Xie , Li Zhang , Shihe Wang , Rongjie Yi , Mengwei Xu This is my paper · ORCID
classification cs.AI
keywords androiddroidcallintentinvocationlanguagedatasetmodelsaccurate
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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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  1. Every Software as an Agent: Blueprint and Case Study

    cs.SE 2025-02 conditional novelty 6.0 of 10

    An LLM agent that writes code and executes it inside the app's runtime can complete tasks that GUI-clicking agents struggle with, with a small case study reporting up to 80% task completion.

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