arXiv:2607.08034v1 Announce Type: new Abstract: Large language models (LLMs) are used worldwide, yet disproportionately reflect Western values, limiting their ability to represent diverse value systems. We introduce PLURAL, a large-scale, value-focused preference dataset grounded in the Integrated Values Survey (IVS), a nationally representative survey spanning 92 countries. Using a two-stage generation pipeline, we transform survey responses into synthetic preference triplets that preserve normative value signals while producing realistic scenarios. We release an initial version of PLURAL con
The increasing global deployment of large language models is highlighting biases towards Western values, necessitating datasets for broader cultural representation.
This development addresses a critical limitation in current AI, enabling the creation of more globally relevant and ethically aligned AI, which is crucial for widespread adoption and trust.
The introduction of PLURAL provides a concrete tool for researchers and developers to train LLMs that reflect a more diverse set of global values, moving beyond Western-centric biases.
- · AI developers in non-Western nations
- · Multinational corporations deploying AI globally
- · Users of AI in diverse cultural contexts
- · Ethical AI research
- · Platforms with culturally monolithic AI models
- · AI models lacking diverse value alignment
The immediate effect is a more nuanced and culturally adaptable generation of large language models.
This will likely lead to increased trust and faster adoption of AI in non-Western regions, potentially challenging the dominance of current Western-developed AI stacks.
Long-term, culturally aligned AI could foster greater digital sovereignty for nations by reducing reliance on models that do not reflect local values, potentially impacting global AI governance structures.
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Read at arXiv cs.CL