Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
Summary: arXiv:2604.06210v1 Announce Type: cross
Introduction
As large language models (LLMs) become increasingly integrated into global applications, aligning their responses with diverse cultural value orientations has become a paramount concern. This alignment is essential for ensuring user safety and enhancing engagement. Traditional evaluation methods, however, face significant challenges, primarily due to their reliance on discriminative, multiple-choice formats that often probe value knowledge rather than authentic orientations.
The $C^3$ Challenge
One of the primary hurdles in evaluating LLMs’ cultural value alignment is known as the Construct-Composition-Context ($C^3$) challenge. This challenge highlights several critical issues:
- Existing benchmarks often overlook the subcultural heterogeneity that exists within broader cultural categories.
- Many current evaluation methods fail to accurately reflect real-world scenarios by not accommodating open-ended generation.
- Current methodologies predominantly assess value knowledge rather than genuine alignment with cultural values.
Introducing DOVE
To address these challenges, we propose DOVE (Distributional Open-Ended Evaluation), a novel evaluation framework designed to compare text distributions generated by LLMs with human-written texts. DOVE offers a more nuanced approach to cultural value alignment by utilizing a rate-distortion variational optimization objective.
This innovative methodology constructs a compact value codebook derived from an extensive corpus of 10,000 documents, effectively mapping textual data into a structured value space. This mapping process serves to filter out semantic noise, enhancing the clarity and relevance of the evaluation.
Measuring Alignment
The alignment of LLM-generated outputs with human values is measured using an unbalanced optimal transport approach. This method captures intra-cultural distributional structures and acknowledges the diversity present within various sub-groups. By focusing on distributional characteristics rather than mere content, DOVE provides a comprehensive evaluation of cultural alignment.
Experimental Validation
In a series of experiments involving 12 different LLMs, the effectiveness of DOVE was rigorously tested. The results indicated that DOVE achieved a predictive validity rate of 31.56% correlation with downstream tasks, showcasing its efficacy in evaluating cultural value alignment. Additionally, the framework demonstrated high reliability, needing as few as 500 samples per cultural group to produce valid results.
Conclusion
The development of DOVE marks a significant advancement in the evaluation of cultural value alignment in LLMs. By addressing the limitations posed by traditional methods and embracing a distributional approach, DOVE not only enhances the reliability of assessments but also fosters a deeper understanding of how LLMs interact with diverse cultural contexts. As LLMs continue to evolve and permeate various aspects of society, frameworks like DOVE will be crucial for ensuring that these technologies align with the rich tapestry of human values.