用知识图谱让大模型更懂不同文化，输出更一致可信。
Toward Culturally Aligned LLMs through Ontology-Guided Multi-Agent Reasoning
- 构建全球文化知识图谱，关联价值观与人口特征
- 多角色推理框架提升跨文化决策一致性，准确率最高提升12.3%
- 适合需要跨文化合规的AI应用开发者
大型语言模型在支持文化敏感决策方面日益重要，但常因预训练数据偏倚和缺乏结构化价值表示而出现偏差。现有方法虽可引导输出，却常缺乏人口学基础，且将价值观视为孤立无关联信号，导致结果不一致、难解释。本文提出基于本体的多智能体推理框架OG-MAR：从世界价值观调查（WVS）中提取个体价值观，通过能力型问题构建固定分类体系下的文化关系网络，形成全球文化本体。推理时，系统检索与本体一致的关系及人口学相似的用户画像，生成多个价值观角色代理，由判断代理综合输出，确保本体一致性和人口相似性。在四个主流大模型上对区域性社会调查基准测试显示，OG-MAR在文化对齐度和鲁棒性上优于对比基线，同时生成更透明的推理路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) increasingly support culturally sensitive decision making, yet often exhibit misalignment due to skewed pretraining data and the absence of structured value representations. Existing methods can steer outputs, but often lack demographic grounding and treat values as independent, unstructured signals, reducing consistency and interpretability. We propose OG-MAR, an Ontology-Guided Multi-Agent Reasoning framework. OG-MAR summarizes respondent-specific values from the World Values Survey (WVS) and constructs a global cultural ontology by eliciting relations over a fixed taxonomy via competency questions. At inference time, it retrieves ontology-consistent relations and demographically similar profiles to instantiate multiple value-persona agents, whose outputs are synthesized by a judgment agent that enforces ontology consistency and demographic proximity. Experiments on regional social-survey benchmarks across four LLM backbones show that OG-MAR improves cultural alignment and robustness over competitive baselines, while producing more transparent reasoning traces.
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