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[2602.17695] EXACT: Explicit Attribute-Guided Decoding-Time Personalization
Summary
The paper presents EXACT, a novel approach for decoding-time personalization in large language models, enhancing user alignment through interpretable attributes and preference feedback.
Why It Matters
As AI systems increasingly interact with diverse user preferences, the ability to personalize responses in real-time is crucial. EXACT addresses limitations of existing methods by providing a more interpretable and adaptable framework, which could significantly improve user experience in applications like chatbots and personalized content generation.
Key Takeaways
- EXACT improves personalization by using interpretable attributes for user preferences.
- The method adapts to changing user contexts without rigid representations.
- Extensive experiments show that EXACT outperforms existing personalization models.
Computer Science > Machine Learning arXiv:2602.17695 (cs) [Submitted on 6 Feb 2026] Title:EXACT: Explicit Attribute-Guided Decoding-Time Personalization Authors:Xin Yu, Hanwen Xing, Lingzhou Xue View a PDF of the paper titled EXACT: Explicit Attribute-Guided Decoding-Time Personalization, by Xin Yu and 2 other authors View PDF HTML (experimental) Abstract:Achieving personalized alignment requires adapting large language models to each user's evolving context. While decoding-time personalization offers a scalable alternative to training-time methods, existing methods largely rely on implicit, less interpretable preference representations and impose a rigid, context-agnostic user representation, failing to account for how preferences shift across prompts. We introduce EXACT, a new decoding-time personalization that aligns generation with limited pairwise preference feedback using a predefined set of interpretable attributes. EXACT first identifies user-specific attribute subsets by maximizing the likelihood of preferred responses in the offline stage. Then, for online inference, EXACT retrieves the most semantically relevant attributes for an incoming prompt and injects them into the context to steer generation. We establish theoretical approximation guarantees for the proposed algorithm under mild assumptions, and provably show that our similarity-based retrieval mechanism effectively mitigates contextual preference shifts, adapting to disparate tasks without pooling confli...