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CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization

Jing Yao, Xiaoyuan Yi, Jindong Wang, Zhicheng Dou, Xing Xie

TL;DR

CAReDiO introduces a two-component framework for culturally aligning LLMs with pluralistic cultures by optimizing data representativeness and distinctiveness. It combines a cultural data synthesis pipeline (38-topic framework, culture-sensitive question adaptation, and cognitive-conflict-driven responses) with a data-selection mechanism that prioritizes high-signal samples, producing CARDSet for five cultures. Empirical results show significant improvements over baselines, with good performance even at low data budgets (as few as 100 samples) and strong open-ended task performance. The approach offers a cost-efficient path to culturally aware LLMs and highlights the value of targeted data construction for cross-cultural alignment.

Abstract

As Large Language Models (LLMs) more deeply integrate into human life across various regions, aligning them with pluralistic cultures is crucial for improving user experience and mitigating cultural conflicts. Existing approaches develop culturally aligned LLMs primarily through fine-tuning with massive carefully curated culture-specific corpora. Nevertheless, inspired by culture theories, we identify two key challenges faced by these datasets: (1) Representativeness: These corpora fail to fully capture the target culture's core characteristics with redundancy, causing computation waste; (2) Distinctiveness: They struggle to distinguish the unique nuances of a given culture from shared patterns across other relevant ones, hindering precise cultural modeling. To handle these challenges, we introduce CAReDiO, a novel cultural data construction framework. Specifically, CAReDiO utilizes powerful LLMs to automatically generate cultural conversation data, where both the queries and responses are further optimized by maximizing representativeness and distinctiveness. Using CAReDiO, we construct a small yet effective dataset, covering five cultures, and compare it with several recent cultural corpora. Extensive experiments demonstrate that our method generates more effective data and enables cultural alignment with as few as 100 training samples, enhancing both performance and efficiency.

CAReDiO: Cultural Alignment of LLM via Representativeness and Distinctiveness Guided Data Optimization

TL;DR

CAReDiO introduces a two-component framework for culturally aligning LLMs with pluralistic cultures by optimizing data representativeness and distinctiveness. It combines a cultural data synthesis pipeline (38-topic framework, culture-sensitive question adaptation, and cognitive-conflict-driven responses) with a data-selection mechanism that prioritizes high-signal samples, producing CARDSet for five cultures. Empirical results show significant improvements over baselines, with good performance even at low data budgets (as few as 100 samples) and strong open-ended task performance. The approach offers a cost-efficient path to culturally aware LLMs and highlights the value of targeted data construction for cross-cultural alignment.

Abstract

As Large Language Models (LLMs) more deeply integrate into human life across various regions, aligning them with pluralistic cultures is crucial for improving user experience and mitigating cultural conflicts. Existing approaches develop culturally aligned LLMs primarily through fine-tuning with massive carefully curated culture-specific corpora. Nevertheless, inspired by culture theories, we identify two key challenges faced by these datasets: (1) Representativeness: These corpora fail to fully capture the target culture's core characteristics with redundancy, causing computation waste; (2) Distinctiveness: They struggle to distinguish the unique nuances of a given culture from shared patterns across other relevant ones, hindering precise cultural modeling. To handle these challenges, we introduce CAReDiO, a novel cultural data construction framework. Specifically, CAReDiO utilizes powerful LLMs to automatically generate cultural conversation data, where both the queries and responses are further optimized by maximizing representativeness and distinctiveness. Using CAReDiO, we construct a small yet effective dataset, covering five cultures, and compare it with several recent cultural corpora. Extensive experiments demonstrate that our method generates more effective data and enables cultural alignment with as few as 100 training samples, enhancing both performance and efficiency.

Paper Structure

This paper contains 42 sections, 1 equation, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Alignment of LLMs
  4. Cultural Alignment of LLMs
  5. Definition and Evaluation
  6. Alignment Approaches
  7. Method
  8. Formalization and Overview
  9. Cultural Data Synthesis Pipeline
  10. Comprehensive cultural framework
  11. Cultural Question Adaptation
  12. Distinctive Response Generation
  13. Cultural Data Selection Strategy
  14. Representativeness Measurement
  15. Distinctiveness Measurement
  16. ...and 27 more sections

Figures (4)

  • Figure 1: Architecture of the CAReDiO framework, including two modules to optimize representativeness and distinctiveness of data for cultural alignment.
  • Figure 2: Results for different # of training samples.
  • Figure 3: Distribution and word clouds of cultural data.
  • Figure 4: Case studies on cultural alignment.