LLM for Mobile: An Initial Roadmap
When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to apply LLMs for the mobile ecosystem. We therefore provide a research roadmap for guiding our fellow researchers to achieve that as a whole. In this roadmap, we sum up six directions that we believe are urgently required for research to enable native intelligence in mobile devices. In each direction, we further summarize the current research progress and the gaps that still need to be filled by our fellow researchers.
Authors
- D. Chen (ORCID: https://orcid.org/0009-0008-0880-4317)
- Yanjie Zhao (ORCID: https://orcid.org/0000-0001-8793-5367)
- Jacques Klein (ORCID: https://orcid.org/0000-0003-4052-475X)
- Mingyi Zhou (ORCID: https://orcid.org/0000-0003-3514-0372)
- Tegawendé François Bissyandé (ORCID: https://orcid.org/0000-0001-7270-9869)
- Shuai Wang (ORCID: https://orcid.org/0000-0002-0866-0308)
- Xiao Chen (ORCID: https://orcid.org/0000-0002-4224-6450)
- Yonghui Liu (ORCID: https://orcid.org/0000-0001-7548-5100)
- Li Li (ORCID: https://orcid.org/0000-0003-2990-1614)
- Haoyu Wang (ORCID: https://orcid.org/0000-0003-1100-8633)
Institutions
- University of Luxembourg (LU)
- Hong Kong University of Science and Technology (HK)
- Huazhong University of Science and Technology (CN)
- Monash University (AU)
- University of Newcastle Australia (AU)
- Beihang University (CN)
Publication Details
- Journal
- ACM Transactions on Software Engineering and Methodology
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1145/3708528
- Citations
- 5
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.75