English(EN) An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift

偏好调优对齐显示出泛化-多样性权衡

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-25 04:00

研究人员对偏好调优（一种用于使语言模型与人类判断对齐的方法）进行了全面研究。该研究调查了这些调优后的模型在新领域的泛化能力以及其输出的多样性。研究结果表明，尽管伪标签等适应策略可以显著降低领域迁移导致的性能下降，但它们也可能导致模式崩溃，凸显了泛化能力和多样性之间的权衡。 AI

影响这项研究突显了当前语言模型对齐技术存在的潜在局限性，表明需要开发能够平衡领域泛化能力和输出多样性的方法。

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arXiv cs.AI TIER_1 English(EN) · Constantinos Karouzos, Xingwei Tan, Nikolaos Aletras · 2026-08-25 04:00

领域迁移下偏好调优泛化与多样性的实证研究

arXiv:2601.05882v2 Announce Type: replace-cross Abstract: Preference tuning aligns base language models to human judgments of quality, helpfulness, or safety by optimizing over explicit preference signals rather than likelihood alone. Prior work has shown that preference tuning d…