# LLMs in Mobile Apps: Practices, Challenges, and Opportunities

arXiv: https://arxiv.org/abs/2502.15908 · HTML: https://mlsanity.com/paper/2502.15908/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 69 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 17 of 17)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024)
- [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://mlsanity.com/paper/2306.02707/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://mlsanity.com/paper/2312.11514/) (2023)
- [If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents](https://mlsanity.com/paper/2401.00812/) (2024)
- [Towards the Law of Capacity Gap in Distilling Language Models](https://mlsanity.com/paper/2311.07052/) (2023)
- [Lifting the Curse of Capacity Gap in Distilling Language Models](https://mlsanity.com/paper/2305.12129/) (2023)
- [MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases](https://mlsanity.com/paper/2406.10290/) (2024)
- [The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Products](https://mlsanity.com/paper/2308.04328/) (2023)
- [A Performance Evaluation of a Quantized Large Language Model on Various Smartphones](https://mlsanity.com/paper/2312.12472/) (2023)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [Studying Ad Library Integration Strategies of Top Free-to-Download Apps](https://mlsanity.com/paper/2104.00182/) (2021)

## Abstract (arXiv)

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.