Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
arXiv:2604.06210v1 Announce Type: new Abstract: 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 sub-group diversity. Experiments across 12 LLMs show that DOVE achieves superior predictive validity,
arXiv:2604.06210v1 Announce Type: new Abstract: 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 sub-group 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.
Executive Summary
The article introduces DOVE (Distributional Open-Ended Evaluation), a novel framework for assessing Large Language Model (LLM) cultural value alignment. Addressing the limitations of existing discriminative benchmarks, DOVE employs a rate-distortion variational optimization to create a 'value-codebook' from 10,000 documents, mapping open-ended text into a structured value space. Alignment is quantified via unbalanced optimal transport, enabling comparison of human-written and LLM-generated text distributions, thereby capturing subcultural heterogeneity and genuine value orientations. The framework demonstrates superior predictive validity and high reliability, marking a significant advancement in evaluating LLM cultural sensitivity and mitigating risks associated with global deployment.
Key Points
- ▸ DOVE framework addresses the Construct-Composition-Context (C³) challenge in LLM cultural value alignment.
- ▸ It utilizes a rate-distortion variational optimization to create a 'value-codebook' from a large corpus, filtering semantic noise.
- ▸ Alignment is measured by comparing text distributions (human vs. LLM) using unbalanced optimal transport, capturing subcultural diversity.
- ▸ DOVE demonstrates superior predictive validity (31.56% correlation with downstream tasks) and high reliability with fewer samples.
- ▸ The framework shifts from probing value knowledge to assessing true, open-ended value orientations.
Merits
Novel Methodological Approach
The use of rate-distortion variational optimization for value-codebook construction and unbalanced optimal transport for distributional comparison represents a significant methodological innovation in LLM evaluation, moving beyond simplistic discriminative formats.
Addresses C³ Challenge
DOVE effectively tackles the 'Construct-Composition-Context' limitations of prior benchmarks by evaluating open-ended generation, capturing subcultural heterogeneity, and probing genuine orientations rather than mere knowledge recall.
Enhanced Predictive Validity
Achieving a 31.56% correlation with downstream tasks, DOVE offers a more robust and practically relevant measure of alignment compared to existing methods, suggesting a stronger link to real-world performance and societal impact.
Efficiency and Reliability
The framework's ability to maintain high reliability with as few as 500 samples per culture is a practical advantage, making large-scale, culturally nuanced evaluations more feasible and resource-efficient.
Demerits
Value-Codebook Construction Bias
The 'value-codebook's' inherent biases derived from the initial 10K document corpus could inadvertently embed specific cultural or ideological perspectives, potentially skewing subsequent alignment evaluations.
Interpretability of 'Value Space'
While mapping text into a 'structured value space' is innovative, the interpretability and explainability of this latent space, particularly for complex cultural nuances, may pose challenges for human understanding and validation.
Generalizability of 'Unbalanced Optimal Transport'
The effectiveness of unbalanced optimal transport in capturing 'intra-cultural distributional structures' across a vast array of global cultures, some with highly subtle or implicit value systems, requires further empirical validation.
Expert Commentary
The DOVE framework represents a significant methodological leap in the critical domain of LLM cultural alignment. By moving beyond simplistic, discriminative evaluations, the authors skillfully address the inherent complexities of value systems, particularly subcultural heterogeneity and the open-ended nature of human expression. The application of rate-distortion variational optimization and unbalanced optimal transport is particularly sophisticated, offering a technically rigorous approach to quantifying alignment. However, the 'value-codebook's' construction and the interpretability of its derived 'value space' warrant deeper scrutiny. The potential for embedding implicit biases from the source corpus, however extensive, remains a non-trivial concern. Legal and ethical considerations surrounding the provenance and representativeness of the 10K documents are paramount. This work lays a strong foundation, but future research must rigorously validate the universality of the 'value space' and address potential biases in its construction to ensure truly equitable and globally acceptable LLM deployment.
Recommendations
- ✓ Conduct an independent audit of the 10K document corpus used for value-codebook construction to assess its cultural diversity, representativeness, and potential biases.
- ✓ Develop mechanisms for human-in-the-loop validation of the 'value space' and its mappings, ensuring interpretability and alignment with established anthropological or sociological frameworks.
- ✓ Explore the application of DOVE across a wider range of linguistic and cultural contexts, including under-represented languages, to test its generalizability and robustness.
- ✓ Investigate the integration of legal and ethical compliance frameworks directly into the DOVE evaluation pipeline, potentially flagging outputs that contravene specific cultural or legal norms.
Sources
Original: arXiv - cs.CL