Adapting large language models (LLMs) to diverse cultural values is a challenging task, as existing LLMs often reflect the values of specific groups by default, and potentially cause harm to others. In this paper, we present CLCA, a novel framework for enhancing LLM alignment with cultural values based on cultural learning. The framework leverages simulated social interactions to generate conversations in which LLMs engage in role-playing within culturally adapted social scenarios, capturing implicit cultural norms for model fine-tuning. CLCA improves cultural value alignment across various model architectures measured using World Value Survey data, demonstrating the effectiveness of our proposed approach. Our results provide early evidence that understanding intent and social interactions can enhance cultural value adaptation in LLMs, highlighting the promise of training approaches based on cultural learning.

Adapting cultural values in Large Language Models (LLMs) presents significant challenges, particularly due to biases and limited training data.Prior work primarily aligns LLMs with different cultural values using World Values Survey (WVS) data.However, it remains unclear whether this approach effectively captures cultural nuances or produces distinct cultural representations for various downstream tasks.In this paper, we systematically investigate WVS-based training for cultural value adaptation and find that relying solely on survey data can homogenize cultural norms and interfere with factual knowledge.To investigate these issues, we augment WVS with encyclopedic and scenario-based cultural narratives from Wikipedia and NormAd.While these narratives may have variable effects on downstream tasks, they consistently improve cultural distinctiveness than survey data alone.Our work highlights the inherent complexity of aligning cultural values to guide task-specific behavior.Code: https://github.com/faridlazuarda/ from-surveys-to-narratives. IntroductionRecent research in Large Language Models (LLMs) suggests LLMs align closely with the cultural values of Western, Educated, Industrialized, Rich, and Democratic (WEIRD, Henrich et al. 2010) societies without adaptations (Johnson et al., 2022;Ramezani and Xu, 2023; Cao et al., 2023, among others).The WEIRD-centric bias can harm specific groups and limit the model's usefulness to a diverse global audience.Indeed, culture is a distinct and vital aspect of human society, influencing behavior, norms, and worldviews (Geertz, 2017).However, current research lacks robust mechanisms to adapt LLMs' outputs in ways that reflect different cultural value systems (i.e., culturally adapt LLMs). 1 1 For this paper, we focus on "culture" at a linguisticregional level (e.g., Iraq and Jordan represent Arab culture

Abstract The surge of interest in culture in NLP has inspired much recent research, but a shared understanding of “culture” remains unclear, making it difficult to evaluate progress in this emerging area. Drawing on prior research in NLP and related fields, we propose a fine-grained taxonomy of elements in culture that can provide a systematic framework for analyzing and understanding research progress. Using the taxonomy, we survey existing resources and methods for culturally aware and adapted NLP, providing an overview of the state of the art and the research gaps that still need to be filled.