English(EN) Pluralistic Alignment in Large Language Models: Objectives, Human References, and Preference Representations

新框架阐明大型语言模型中的多元对齐

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

一篇新的arXiv论文介绍了一个理解大型语言模型中多元对齐的框架。该论文提出了一个两层分类法，以区分展示一系列人类支持的观点、适应特定偏好以及匹配人口响应分布。它还通过聚合人口、国家或亚群体来对人类参考进行分类。作者综合了70篇相关作品的语料库，发现当前研究严重偏向可控和分布多元主义，而Overton多元主义的探索较少。该论文将这些目标与其验证所需的证据联系起来，旨在阐明当前方法并指导未来在AI系统中表示分歧的研究。 AI

影响为开发能够表示不同人类偏好的LLM提供了一种结构化方法，可能带来更细致和适应性更强的AI系统。

排序理由该集群包含一篇在arXiv上发表的学术论文，详细介绍了AI对齐的新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Meng-Chen Wu, Umesh Bodhwani · 2026-10-23 04:00

Pluralistic Alignment in Large Language Models: Objectives, Human References, and Preference Representations

arXiv:2610.24471v2 Announce Type: replace-cross Abstract: Pluralistic alignment aims to represent meaningful variation in human preferences rather than collapse heterogeneous judgments into a single target. Yet existing work often mixes three distinct questions: what pluralistic …

本文如何被排名

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Pluralistic Alignment in Large Language Models: Objectives, Human References, and Preference Representations

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