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Open access Aug 2026

Statistical impossibility and possibility of aligning LLMs with human preferences: From Condorcet paradox to Nash equilibrium

Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying these models for decision-making. In this paper, we seek to uncover fundamental statistical limits concerning aligning LLMs with human preferences, with a focus on the probabilisti...

Kaizhao Liu, Qi Long, Zhekun Shi et al. · 0 citations

Optimal Estimation of Watermark Proportions in Hybrid AI-Human Texts

This paper addresses the problem of optimally estimating the watermark proportion in mixed-source texts, and proposes efficient estimators for this class of methods, and derive minimax lower bounds for any measurable estimator based on pivotal statistics, showing that their estimators achieve these lower bounds.

Xiang Li, Garrett Wen, Weiqing He et al. · 5 citations · ⚡1

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