PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning
Xiaoyou Liu, Xinyi Mou, Shengbin Yue, Liang Wang, Yuqing Wang, Qiexiang Wang, Tianrui Qin, Zhongyu Wei

TL;DR
PersonaDual is a framework that adaptively balances personalized and objective reasoning in large language models, improving factual correctness while maintaining personalization.
Contribution
It introduces a dual-mode training and a reinforcement learning method to switch between personalized and objective reasoning modes effectively.
Findings
Achieves near interference-free performance in balancing personalization and objectivity.
Improves objective problem-solving by leveraging personalized signals.
Reduces interference from personalization in large language models.
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…
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Taxonomy
TopicsPersona Design and Applications · Recommender Systems and Techniques · Innovative Human-Technology Interaction
