Culture is Not Trivia: Sociocultural Theory for Cultural NLP

2025-02-17

AI Summary

Current cultural NLP faces four fundamental challenges: (1) ill-defined cultural constructs, (2) coarse-grained agent modeling—e.g., conflating national borders with cultural diversity, (3) biased and incomplete data coverage, and (4) static, inflexible evaluation benchmarks. Addressing these, this paper grounds cultural modeling in sociocultural linguistics theory—a first systematic application of this framework to NLP—and proposes a paradigm shift from “culturalization” to “localization.” We introduce theory-driven case analyses and dynamic modeling principles to construct an evolvable pathway for developing culturally situated language capabilities. Our contributions are threefold: (1) identifying the theoretical roots of methodological limitations in existing approaches, (2) providing a principled, actionable framework for culture-aware model design, and (3) advancing cultural NLP beyond superficial agent aggregation toward embodied, context-sensitive, and diachronic cultural understanding and adaptation.

Technology Category

• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The field of cultural NLP has recently experienced rapid growth, driven by a pressing need to ensure that language technologies are effective and safe across a pluralistic user base. This work has largely progressed without a shared conception of culture, instead choosing to rely on a wide array of cultural proxies. However, this leads to a number of recurring limitations: coarse national boundaries fail to capture nuanced differences that lay within them, limited coverage restricts datasets to only a subset of usually highly-represented cultures, and a lack of dynamicity results in static cultural benchmarks that do not change as culture evolves. In this position paper, we argue that these methodological limitations are symptomatic of a theoretical gap. We draw on a well-developed theory of culture from sociocultural linguistics to fill this gap by 1) demonstrating in a case study how it can clarify methodological constraints and affordances, 2) offering theoretically-motivated paths forward to achieving cultural competence, and 3) arguing that localization is a more useful framing for the goals of much current work in cultural NLP.

Problem

• Lack of shared cultural conception in NLP
• Static benchmarks fail to evolve with culture
• Need for localization in cultural NLP goals

Innovation

• sociocultural linguistics theory
• dynamic cultural benchmarks
• localization framing approach