Identity-Aware Large Language Models require Cultural Reasoning

TL;DR

This paper emphasizes the importance of cultural reasoning in large language models, highlighting current limitations and proposing the need for foundational cultural competence to improve identity-awareness and social norm alignment.

Contribution

It defines cultural reasoning for language models, critiques current evaluation methods, and advocates for its development as a core capability for identity-aware AI.

Findings

Models default to Western norms in moral and idiomatic judgments.

Fine-tuning reduces but does not eliminate cultural biases.

Current static accuracy metrics are insufficient for assessing cultural reasoning.

Abstract

Large language models have become the latest trend in natural language processing, heavily featuring in the digital tools we use every day. However, their replies often reflect a narrow cultural viewpoint that overlooks the diversity of global users. This missing capability could be referred to as cultural reasoning, which we define here as the capacity of a model to recognise culture-specific knowledge values and social norms, and to adjust its output so that it aligns with the expectations of individual users. Because culture shapes interpretation, emotional resonance, and acceptable behaviour, cultural reasoning is essential for identity-aware AI. When this capacity is limited or absent, models can sustain stereotypes, ignore minority perspectives, erode trust, and perpetuate hate. Recent empirical studies strongly suggest that current models default to Western norms when judging moral dilemmas, interpreting idioms, or offering advice, and that fine-tuning on survey data only partly reduces this tendency. The present evaluation methods mainly report static accuracy scores and thus fail to capture adaptive reasoning in context. Although broader datasets can help, they cannot alone ensure genuine cultural competence. Therefore, we argue that cultural reasoning must be treated as a foundational capability alongside factual accuracy and linguistic coherence. By clarifying the concept and outlining initial directions for its assessment, a foundation is laid for future systems to be able to respond with greater sensitivity to the complex fabric of human culture.