DroidCall: A Dataset for LLM-powered Android Intent Invocation

ACL ARR 2025 February Submission5931 Authors

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://anonymous.4open.science/r/DroidCall-C100.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: Dialogue and Interactive Systems, Generation, Human-Centered NLP, Language Modeling, NLP Applications

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models

Languages Studied: English

Submission Number: 5931