arXiv cs.CL· Meng-Chen Wu, Umesh Bodhwani·· 13 小时前AI 评分31
大语言模型中的多元对齐：目标、人类参照与偏好表示
Pluralistic Alignment in Large Language Models: Objectives, Human References, and Preference Representations
AI 导读
一篇 arXiv 综述用两层分类法梳理大语言模型多元对齐研究：第一层区分 Overton 多元主义、可引导多元主义与分布多元主义，第二层区分聚合人群、国家地区和子群体三类人类参照。作者编码了 70 篇相关文献，发现现有工作集中于可引导与分布多元主义，符合协议的 Overton 多元主义仅出现在聚合人群层面。文章还为每种目标对应了所需证据，包括视角有效性、条件保真度与分布保真度。
正文 · 原文
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 behavior a model should exhibit, whose preferences define the reference, and what evidence is sufficient to demonstrate success. We organize the literature around a two-layer taxonomy. Layer 1 distinguishes representing a range of human-supported viewpoints (Overton Pluralism), adapting faithfully to a specified perspective or preference condition (Steerable Pluralism), and matching the response distribution observed in a target population (Distributional Pluralism). Layer 2 distinguishes aggregate populations, countries and regions, and subgroups as human references. Preference representation, implementation mechanism, modality, and evidence quality are treated as cross-cutting design choices rather than additional forms of pluralism. We synthesize a protocol-bounded corpus of 70 works on pluralistic alignment and neighboring areas, using the objective-by-human-reference matrix to map existing approaches and identify underexplored combinations. The coded literature concentrates on Steerable Pluralism and Distributional Pluralism, while eligible Overton Pluralism work appears only at the aggregate-population level under our stated protocol. We then connect each objective to the evidence needed to support it, including perspective validity, conditional fidelity, distributional fidelity, support preservation, and uncertainty in subgroup estimates. Together, the taxonomy and evaluation framework clarify what current methods establish, where their claims exceed their evidence, and how future systems can represent disagreement without mistaking variation for faithful representation.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.24471 [cs.AI] |
| (or arXiv:2610.24471v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.24471 arXiv-issued DOI via DataCite |
Submission history
From: Meng-Chen Wu [view email]
[v1]
Sat, 17 Oct 2026 05:32:24 UTC (6,598 KB)
[v2]
Thu, 22 Oct 2026 02:38:58 UTC (6,598 KB)
来源：arXiv cs.CL · arxiv.org