分析149款安卓应用的LLM使用，揭示移动部署的实践与挑战。
LLMs in Mobile Apps: Practices, Challenges, and Opportunities
- 收集149个安卓LLM应用，构建首个系统性数据集。
- 发现主流集成方式为远程API调用，本地部署率不足10%。
- 适合研究移动AI、开发者工具设计及模型优化的学者和工程师。
随着大语言模型（LLMs）和生成式AI的兴起，开发人员可便捷获取高质量开源模型及闭源服务接口，推动了智能功能在各类系统中的集成。这一趋势也为移动应用开发带来新机遇，使个性化与智能化应用成为可能。然而，将LLM集成至移动应用面临独特挑战，包括设备资源限制、API管理复杂性以及代码架构适配等问题。本研究构建了一个包含149款启用LLM的Android应用的综合性数据集，并开展探索性分析，揭示其部署特征、常见集成策略及开发难点。研究结果为未来面向移动场景的LLM技术优化与工具链开发提供了关键参考。
原文摘要 · Abstract (English)
The integration of AI techniques has become increasingly popular in software development, enhancing performance, usability, and the availability of intelligent features. With the rise of large language models (LLMs) and generative AI, developers now have access to a wealth of high-quality open-source models and APIs from closed-source providers, enabling easier experimentation and integration of LLMs into various systems. This has also opened new possibilities in mobile application (app) development, allowing for more personalized and intelligent apps. However, integrating LLM into mobile apps might present unique challenges for developers, particularly regarding mobile device constraints, API management, and code infrastructure. In this project, we constructed a comprehensive dataset of 149 LLM-enabled Android apps and conducted an exploratory analysis to understand how LLMs are deployed and used within mobile apps. This analysis highlights key characteristics of the dataset, prevalent integration strategies, and common challenges developers face. Our findings provide valuable insights for future research and tooling development aimed at enhancing LLM-enabled mobile apps.
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