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LLMs in Mobile Apps: Practices, Challenges, and Opportunities

arXiv 2502.15908 ↗First posted 21 Feb 2025cs.SE cs.CLcited by 0

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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.

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.

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References · 17

  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2024Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phonecited by 1,917
  3. 2024Mixtral of Expertscited by 1,823
  4. 2023A Comprehensive Overview of Large Language Modelscited by 648
  5. 2023Textbooks Are All You Need II: phi-1.5 technical reportcited by 565
  6. 2024Large Language Models: A Surveycited by 525
  7. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 379
  8. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 181
  9. 2023LLM in a flash: Efficient Large Language Model Inference with Limited Memorycited by 177
  10. 2024If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agentscited by 79
  11. 2023Towards the Law of Capacity Gap in Distilling Language Modelscited by 44
  12. 2023Lifting the Curse of Capacity Gap in Distilling Language Modelscited by 25
5 more
  1. 2024MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Casescited by 14
  2. 2023The Product Beyond the Model -- An Empirical Study of Repositories of Open-Source ML Productscited by 10
  3. 2023A Performance Evaluation of a Quantized Large Language Model on Various Smartphonescited by 7
  4. 2024A Large-Scale Study of Model Integration in ML-Enabled Software Systemscited by 6
  5. 2021Studying Ad Library Integration Strategies of Top Free-to-Download Appscited by 3

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