DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

Abstract

The alignment of large language models (LLMs) with human values is critical for their safe and effective deployment across diverse user populations. However, existing benchmarks often neglect cultural and demographic diversity, leading to limited understanding of how value alignment generalizes globally. In this work, we introduce DiverValue-Bench, a benchmark that systematically evaluates LLMs’ alignment with multi-dimensional human value preferences across 74 countries/regions. DiverValue-Bench contains 23,763 high-quality instances annotated with fine-grained value labels, personalized questions, and rich demographic metadata, providing broad demographic and geographic coverage for population-aware value-alignment evaluation. Using DiverValue-Bench, we conduct an in-depth analysis of several representative LLMs, revealing substantial disparities in alignment performance across geographic and demographic lines. We further demonstrate that lightweight fine-tuning methods, such as Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO), can significantly enhance value alignment in both in-domain and out-of-domain settings. Our findings underscore the necessity for population-aware alignment evaluation and provide actionable insights for building culturally adaptive and value-sensitive LLMs. DiverValue-Bench serves as a practical foundation for future research on global alignment, personalized value modeling, and equitable AI development.