DroidCall: A Dataset for LLM-powered Android Intent Invocation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929609423978496 |
|---|---|
| author | Xie, Weikai Zhang, Li Wang, Shihe Yi, Rongjie Xu, Mengwei |
| author_facet | Xie, Weikai Zhang, Li Wang, Shihe Yi, Rongjie Xu, Mengwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00402 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DroidCall: A Dataset for LLM-powered Android Intent Invocation Xie, Weikai Zhang, Li Wang, Shihe Yi, Rongjie Xu, Mengwei Artificial Intelligence 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. |
| title | DroidCall: A Dataset for LLM-powered Android Intent Invocation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.00402 |