As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement. However, existing benchmarks face the Construct-Composition-Context ($C^3$) challenge: relying on discriminative, multiple-choice formats that probe value knowledge rather than true orientations, overlook subcultural heterogeneity, and mismatch with real-world open-ended generation. We introduce DOVE, a distributional evaluation framework that directly compares human-written text distributions with LLM-generated outputs. DOVE utilizes a rate-distortion variational optimization objective to construct a compact value codebook from 10K documents, mapping text into a structured value space to filter semantic noise. Alignment is measured using unbalanced optimal transport, capturing intra-cultural distributional structures and subgroup diversity. Experiments across 12 LLMs show that DOVE achieves superior predictive validity, attaining a 31.56% correlation with downstream tasks, while maintaining high reliability with as few as 500 samples per culture.

翻译：随着大语言模型的全球部署，使其文化价值取向对齐对于安全与用户参与至关重要。然而，现有基准面临构造-构成-上下文（$C^3$）挑战：依赖区分式、多项选择形式，考察的是价值知识而非真实取向，忽视亚文化异质性，且与现实世界的开放式生成不匹配。我们提出DOVE，一种直接比较人类文本分布与LLM生成输出的分布式评估框架。DOVE利用率失真变分优化目标从1万份文档中构建紧凑的价值码本，将文本映射至结构化价值空间以滤除语义噪声。采用非平衡最优传输测量对齐度，捕捉文化内部分布结构及子群体多样性。在12个LLM上的实验表明，DOVE具有优越的预测效度，与下游任务相关性达31.56%，同时每文化仅需500个样本即可保持高可靠性。