PLURAL: A Global Dataset for Value Alignment

2026-07-11 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

PLURAL is a new large-scale, value-focused preference dataset designed to address the disproportionate reflection of Western values in large language models (LLMs). Grounded in the Integrated Values Survey (IVS), which covers 92 countries, PLURAL uses a two-stage generation pipeline to create synthetic preference triplets from survey responses, preserving normative value signals and realistic scenarios. The initial release contains approximately 500,000 preference triplets representing people from 20 diverse countries. Evaluations confirm PLURAL's effectiveness: dataset-level validation shows it preserves both cross-country value differences and within-country diversity. Automated evaluation demonstrates that training on PLURAL improves alignment with target cultural profiles, reducing mean absolute error by up to 27.7% compared to strong baselines. Furthermore, blind human evaluations with 176 participants in India, Brazil, and Japan judged PLURAL-aligned responses as more representative of their national values, indicating its potential for scalable pluralistic alignment.

Key takeaway

For machine learning engineers developing global LLM applications, PLURAL offers a critical resource for achieving pluralistic value alignment. You should integrate this dataset into your fine-tuning pipelines to mitigate Western bias and ensure your models better represent diverse cultural profiles. This approach can significantly improve model acceptance and relevance across different user demographics, as demonstrated by up to a 27.7% reduction in alignment error.

Key insights

PLURAL enables large language models to align with diverse global value systems through a novel preference dataset.

Principles

• LLMs currently reflect disproportionately Western values.
• Value alignment requires culturally diverse preference data.
• Synthetic data can preserve normative value signals.

Method

A two-stage generation pipeline converts Integrated Values Survey responses into synthetic preference triplets, preserving normative value signals for LLM training.

In practice

• Train LLMs on PLURAL to improve alignment with target cultural profiles.
• Utilize PLURAL to reduce mean absolute error in value steering by up to 27.7%.

Topics

• Large Language Models
• Value Alignment
• Preference Datasets
• Cultural Bias
• Synthetic Data
• PLURAL Dataset

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

Related on AIssential

• PLURAL: A Global Dataset for Value Alignment — PLURAL is a dataset enabling LLMs to align with diverse global values, validated by automated and human evaluations.
• PLURAL: A Global Dataset for Value Alignment — PLURAL is a global dataset enabling LLMs to align with diverse cultural values, reducing Western bias.
• LKValues: Aligning Large Language Models with Sri Lankan Societal Values — Culturally biased LLMs necessitate country- and language-specific alignment resources to handle local societal values effectively.
• VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models — VALUEFLOW unifies LLM value extraction, evaluation, and steering with calibrated intensity control, addressing pluralistic alignment challenges.
• Cross-Lingual Consensus: Aligning Multilingual Cultural Knowledge via Multilingual Self-Consistency — LLMs' latent cultural knowledge in local languages can be surfaced and transferred to improve English query performance and reduce Western bias.
• Evaluating Pluralism in LLMs through Latent Perspectives — LLMs struggle to represent diverse perspectives, especially rarer ones, diverging from human text distributions.

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.