Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
arXiv:2604.06210v3 Announce Type: replace 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
The global deployment of LLMs necessitates robust evaluation methods for cultural alignment, especially as previous benchmarks have limitations in assessing true value orientations.
Ensuring LLMs are culturally aligned is critical for safety and user engagement, directly impacting their commercial viability and societal acceptance.
The introduction of DOVE allows for a more accurate and open-ended evaluation of LLM cultural value alignment, moving beyond simplistic multiple-choice formats.
- · AI developers focused on ethical deployment
- · Multinational corporations using LLMs
- · Users in diverse cultural contexts
- · LLMs with unaligned cultural values
- · Outdated LLM evaluation methodologies
Improved cultural alignment in deployed LLMs reduces instances of bias and misunderstanding.
Enhanced trust in LLMs facilitates broader adoption across diverse global markets and applications.
The ability to accurately measure and adapt cultural values becomes a key competitive differentiator for LLM providers, influencing market share and regulatory frameworks.
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Read at arXiv cs.CL