@inproceedings{liu-etal-2026-personadual,
title = "{P}ersona{D}ual: Balancing Personalization and Objectivity via Adaptive Reasoning",
author = "Liu, Xiaoyou and
Mou, Xinyi and
Yue, Shengbin and
Wang, Liang and
Wang, Yuqing and
Wang, Qiexiang and
Qin, Tianrui and
Wei, Zhongyu",
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.355/",
pages = "8144--8161",
ISBN = "979-8-89176-459-0",
abstract = "As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can improve interaction but may compromise objectivity and factual correctness, especially when it is misaligned with the question. To alleviate this problem, we propose PersonaDual, a framework that supports both general-purpose objective reasoning and personalized reasoning in a single model, and adaptively switches modes based on context. PersonaDual is first trained with SFT to learn two reasoning patterns, and then further optimized via reinforcement learning with our proposed DualGRPO to improve mode selection. Experiments on objective and personalized benchmarks show that PersonaDual preserves the benefits of personalization while reducing interference, achieving near interference-free performance and better leveraging helpful personalized signals to improve objective problem-solving."
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<abstract>As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can improve interaction but may compromise objectivity and factual correctness, especially when it is misaligned with the question. To alleviate this problem, we propose PersonaDual, a framework that supports both general-purpose objective reasoning and personalized reasoning in a single model, and adaptively switches modes based on context. PersonaDual is first trained with SFT to learn two reasoning patterns, and then further optimized via reinforcement learning with our proposed DualGRPO to improve mode selection. Experiments on objective and personalized benchmarks show that PersonaDual preserves the benefits of personalization while reducing interference, achieving near interference-free performance and better leveraging helpful personalized signals to improve objective problem-solving.</abstract>
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%0 Conference Proceedings
%T PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning
%A Liu, Xiaoyou
%A Mou, Xinyi
%A Yue, Shengbin
%A Wang, Liang
%A Wang, Yuqing
%A Wang, Qiexiang
%A Qin, Tianrui
%A Wei, Zhongyu
%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-personadual
%X As users increasingly expect LLMs to align with their preferences, personalized information becomes valuable. However, personalized information can be a double-edged sword: it can improve interaction but may compromise objectivity and factual correctness, especially when it is misaligned with the question. To alleviate this problem, we propose PersonaDual, a framework that supports both general-purpose objective reasoning and personalized reasoning in a single model, and adaptively switches modes based on context. PersonaDual is first trained with SFT to learn two reasoning patterns, and then further optimized via reinforcement learning with our proposed DualGRPO to improve mode selection. Experiments on objective and personalized benchmarks show that PersonaDual preserves the benefits of personalization while reducing interference, achieving near interference-free performance and better leveraging helpful personalized signals to improve objective problem-solving.
%U https://aclanthology.org/2026.emnlp-main.355/
%P 8144-8161
Markdown (Informal)
[PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning](https://aclanthology.org/2026.emnlp-main.355/) (Liu et al., EMNLP 2026)
ACL
- Xiaoyou Liu, Xinyi Mou, Shengbin Yue, Liang Wang, Yuqing Wang, Qiexiang Wang, Tianrui Qin, and Zhongyu Wei. 2026. PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 8144–8161, Budapest, Hungary. Association for Computational Linguistics.