@inproceedings{liu-etal-2026-text,
title = "Text as a Universal Interface for Transferable Personalization",
author = "Liu, Yuting and
Guan, Jian and
Li, Jia-Nan and
Wu, Wei and
Yang, Jiang-Ming and
Zhao, Jianzhe and
Guo, Guibing",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.695/",
pages = "15977--16018",
ISBN = "979-8-89176-459-0",
abstract = "We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yielding opaque ``black-box'' profiles that are difficult to interpret and transfer across models and tasks. In contrast, we advocate natural language as a universal, model- and task-agnostic interface for preference representation. The formulation leads to interpretable and reusable preference descriptions, while naturally supporting continual evolution as new interactions are observed. To learn such representations, we introduce a two-stage training framework that combines supervised fine-tuning on high-quality synthesized data with reinforcement learning to optimize long-term utility and cross-task transferability. Based on this framework, we develop \textsc{AlignXplore+}, a universal preference reasoning model that generates textual preference summaries. Experiments on nine benchmarks show that our 8B model achieves state-of-the-art performance{---}outperforming substantially larger open-source models{---}while exhibiting strong transferability across tasks, model families, and interaction formats. Code and dataset are accessible at \url{https://github.com/AntResearchNLP/AlignX-Family/tree/main/AlignXplorePlus}."
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%0 Conference Proceedings
%T Text as a Universal Interface for Transferable Personalization
%A Liu, Yuting
%A Guan, Jian
%A Li, Jia-Nan
%A Wu, Wei
%A Yang, Jiang-Ming
%A Zhao, Jianzhe
%A Guo, Guibing
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-text
%X We study the problem of personalization in large language models (LLMs). Prior work predominantly represents user preferences as implicit, model-specific vectors or parameters, yielding opaque “black-box” profiles that are difficult to interpret and transfer across models and tasks. In contrast, we advocate natural language as a universal, model- and task-agnostic interface for preference representation. The formulation leads to interpretable and reusable preference descriptions, while naturally supporting continual evolution as new interactions are observed. To learn such representations, we introduce a two-stage training framework that combines supervised fine-tuning on high-quality synthesized data with reinforcement learning to optimize long-term utility and cross-task transferability. Based on this framework, we develop AlignXplore+, a universal preference reasoning model that generates textual preference summaries. Experiments on nine benchmarks show that our 8B model achieves state-of-the-art performance—outperforming substantially larger open-source models—while exhibiting strong transferability across tasks, model families, and interaction formats. Code and dataset are accessible at https://github.com/AntResearchNLP/AlignX-Family/tree/main/AlignXplorePlus.
%U https://aclanthology.org/2026.emnlp-main.695/
%P 15977-16018
Markdown (Informal)
[Text as a Universal Interface for Transferable Personalization](https://aclanthology.org/2026.emnlp-main.695/) (Liu et al., EMNLP 2026)
ACL
- Yuting Liu, Jian Guan, Jia-Nan Li, Wei Wu, Jiang-Ming Yang, Jianzhe Zhao, and Guibing Guo. 2026. Text as a Universal Interface for Transferable Personalization. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15977–16018, Budapest, Hungary. Association for Computational Linguistics.