Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values

2026-09-08

AI Summary

研究通过构建多语言对比探针数据集C-Voices和提出无需微调的价值向量引导方法，解决大语言模型在不同语言中对中文社会价值观一致性的挑战。

Abstract

As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical priority. However, whether LLMs exhibit consistent value preferences across languages remains underexplored, particularly for culturally grounded values, which are more abstract and difficult to evaluate and align than safety-centric principles. We investigate this issue through Chinese Social Values (CSV), a value system rooted in Chinese culture and comprising $12$ dimensions across national, societal, and personal levels. We construct C-Voices, the first comprehensive multilingual contrastive probe dataset for CSV, with 86,400 dilemma-based instances in six languages, each pairing a CSV-aligned action with a value-conflicting alternative. Building on the contrastive probes of C-Voices, we then propose a fine-tuning-free value vector steering method that derives value directions from hidden-state discrepancies and selectively intervenes on value-sensitive layers during inference. Experiments on six languages show that CSV-oriented preferences are model-dependent and language-sensitive, with the same dilemma eliciting divergent responses across languages. Our method achieves effective CSV steering, supports cross-lingual transfer of value vectors, and generalizes to existing FLAMES and ValuePrism.

Problem

• Large Language Models
• Chinese Social Values
• multilingual
• value alignment
• cultural values

Innovation

• multilingual probing
• value vector steering
• CSV (Chinese Social Values)
• hidden-state discrepancies
• cross-lingual transfer