探究大模型在不同语言中是否一致传递中文社会价值观，提出无需微调的干预方法。
Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values
- 构建首个多语言对比探测数据集C-Voices，含86,400个跨语言价值困境实例。
- 发现相同问题在不同语言下响应差异明显，模型对中文价值观偏好具有语言敏感性。
- 提出无微调的价值向量引导方法，可跨语言迁移并适配现有价值评估框架。
随着大语言模型深度融入社会，使其与多元社会价值观对齐成为关键挑战。然而，模型在不同语言间是否保持一致的价值偏好仍缺乏研究，尤其针对根植于文化的抽象价值观。本文聚焦中文社会价值观（CSV），其涵盖国家、社会与个人三个层面共12个维度。构建了首个多语言对比探测数据集C-Voices，包含六种语言的86,400个基于困境的实例，每对实例分别呈现符合与违背CSV的行为选择。基于C-Voices的对比探针，提出一种无需微调的价值向量引导方法，通过隐藏层差异提取价值方向，并在推理时选择性干预敏感层。实验表明，中文价值观偏好具有模型依赖性和语言敏感性，同一困境在不同语言中引发显著不同的回应。所提方法有效实现对中文价值观的引导，支持价值向量跨语言迁移，并可泛化至现有FLAMES与ValuePrism框架。
原文摘要 · Abstract (English)
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.
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