arXiv AI By Zhiyu An, Ziyi Xuan, Yu Yang, Wan Du

Vision: "What Don't I Know About User's Preferences?" How Uncertainty Estimation Improves Proactive Assistant Alignment

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arXiv Computation and Language
5d ago

Adaptive Personalization Under Preference Epistemic Uncertainty

The paper introduces the concept of adaptive personalization under preference epistemic uncertainty, arguing that conversational AI must adjust its response strategy as human preferences evolve. A study shows people prefer state‑contingent strategies when uncertainty changes, and the authors build a multilingual benchmark to test language models on this task. Results reveal a recognition‑generation gap: models can choose the right strategy from options but fail to generate it correctly, defaulting to exploit‑first behavior; targeted training on internal strategy representations improves performance on out‑of‑distribution personas.

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arXiv AI
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Align AI to Dynamic Human-AI Workflows

arXiv:2607. 14240v1 Announce Type: new Abstract: Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature of real-world human-AI interactions.

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