Translating Across Cultures: LLMs for Intralingual Cultural Adaptation

TL;DR

This paper explores how large language models can be used for cultural adaptation in translation, evaluating their ability to modify references to suit different cultures and analyzing their cross-cultural knowledge.

Contribution

It defines the task of cultural adaptation in translation, creates an evaluation framework for LLMs, and analyzes their cross-cultural understanding and potential issues.

Findings

LLMs possess embedded cultural knowledge useful for adaptation

Evaluation framework reveals strengths and limitations of LLMs in cultural tasks

Analysis highlights challenges in automatic cultural adaptation

Abstract

LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.