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    <abstract>Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce personalized text. While lightweight large language models (LLMs) make on-device auxiliary generation feasible, enabling deeply personalized, privacy-preserving, and real-time generative IMEs poses fundamental challenges. To this end, we present HUOZIIME, a personalized on-device IME powered by LLM. We endow HUOZIIME with initial human-like prediction ability by post-training a base LLM on synthesized personalization data. Notably, a hierarchical memory mechanism is designed to continually capture and leverage user-specific input history. Furthermore, we perform systemic optimizations tailored to on-device LLM-based IME deployment, ensuring efficient and responsive operation under mobile constraints. Experiments demonstrate efficient on-device execution and high-fidelity memory-driven personalization. Code and package are available at https://github.com/Shan-HIT/HuoziIME.</abstract>
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%0 Conference Proceedings
%T HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization
%A Shan, Baocai
%A Xu, Yuzhuang
%A Che, Wanxiang
%Y Durrett, Greg
%Y Jian, Ping
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-392-0
%F shan-etal-2026-huoziime
%X Mobile input method editors (IMEs) are the primary interface for text input, yet they remain constrained to manual typing and struggle to produce personalized text. While lightweight large language models (LLMs) make on-device auxiliary generation feasible, enabling deeply personalized, privacy-preserving, and real-time generative IMEs poses fundamental challenges. To this end, we present HUOZIIME, a personalized on-device IME powered by LLM. We endow HUOZIIME with initial human-like prediction ability by post-training a base LLM on synthesized personalization data. Notably, a hierarchical memory mechanism is designed to continually capture and leverage user-specific input history. Furthermore, we perform systemic optimizations tailored to on-device LLM-based IME deployment, ensuring efficient and responsive operation under mobile constraints. Experiments demonstrate efficient on-device execution and high-fidelity memory-driven personalization. Code and package are available at https://github.com/Shan-HIT/HuoziIME.
%U https://aclanthology.org/2026.acl-demo.32/
%P 327-335

Markdown (Informal)

[HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization](https://aclanthology.org/2026.acl-demo.32/) (Shan et al., ACL 2026)

• HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization (Shan et al., ACL 2026)

ACL

• Baocai Shan, Yuzhuang Xu, and Wanxiang Che. 2026. HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 327–335, San Diego, California, United States. Association for Computational Linguistics.