# 大语言模型引导空间能否复现人类价值观几何结构？

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-05 08:00
- AIHOT 分数：43
- AIHOT 链接：https://aihot.news/items/cmttkjron0cazrofplwyi93nm
- 原文链接：https://arxiv.org/abs/2609.06289

## AI 摘要

一项新研究检验大语言模型激活引导向量的潜在几何结构是否反映人类价值观理论框架。基于Schwartz基本人类价值观理论，研究者引入覆盖20种价值观的26K样本基准，发现分布驱动方法（CAA、SphericalSteer等）能恢复与理论预测一致的价值拓扑（Spearman ρ最高0.51），而行为中心方法相关性较低；几何保真度随模型规模提升但指令微调后下降。

## 正文

As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuning methods (e.g., RLHF, DPO) for behavioral control. However, existing work typically validates steering on isolated behaviors, leaving it unclear whether steering vectors encode coherent semantic structure or merely exploit behavior-specific shortcuts. We investigate whether the latent geometry of LLM steering vectors reflects theory-specified structure in human values and morality. Using Schwartz's Theory of Basic Human Values as our primary fine-grained framework, we introduce a 26K-sample benchmark covering 20 human values and analyze distribution-driven methods (e.g., CAA, SphericalSteer, ODESteer) and behavior-centric approaches (e.g., COLD-Steer, BiPO) across diverse model families and sizes. We find that distribution-driven methods recover human value topologies aligned with theoretical predictions (Spearman ρ up to 0.51, p < 10^{-13}). In contrast, behavior-centric methods achieve comparable steering performance but show little correlation with the expected value geometry. Geometric fidelity improves with model scale but drops after instruction tuning. Finally, better geometric alignment also leads to more human-consistent transfer across values: steering one value correctly lifts compatible values and suppresses opposing ones. Code and data are available at: https://github.com/DeepRCL/Steering_Geometry.