  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. Cultural adaptation has applications across several creative
industries and requires intimate knowledge of source and target cultures during
translation. 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 benchmark different models for this task. We 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 including cultural
biases and stereotypes. We hope that this task will offer more insight into the
cultural understanding of LLMs and their creativity in cross-cultural
scenarios.
