LLMs in Mobile Apps: Practices, Challenges, and Opportunities
Kimberly Hau, Safwat Hassan, Shurui Zhou

TL;DR
This paper explores how large language models are integrated into mobile apps, analyzing a dataset of 149 Android applications to identify practices, challenges, and opportunities for future development.
Contribution
It provides a comprehensive analysis of LLM integration in mobile apps, highlighting prevalent strategies and challenges faced by developers.
Findings
Identification of common LLM integration practices in mobile apps.
Insights into challenges like device constraints and API management.
Guidance for future research and tooling development.
Abstract
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…
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Taxonomy
TopicsFinTech, Crowdfunding, Digital Finance · Digital Rights Management and Security
