Survey of Cultural Awareness in Language Models: Text and Beyond
Authors: name: Siddhesh Pawar, name: Junyeong Park, name: Jiho Jin, name: Arnav Arora, name: Junho Myung, name: Srishti Yadav, name: Faiz Ghifari Haznitrama, name: Inhwa Song, name: Alice Oh, name: Isabelle Augenstein

Large-scale deployment of large language models (LLMs) in various
applications, such as chatbots and virtual assistants, requires LLMs to be
culturally sensitive to the user to ensure inclusivity. Culture has been widely
studied in psychology and anthropology, and there has been a recent surge in
research on making LLMs more culturally inclusive in LLMs that goes beyond
multilinguality and builds on findings from psychology and anthropology. In
this paper, we survey efforts towards incorporating cultural awareness into
text-based and multimodal LLMs. We start by defining cultural awareness in
LLMs, taking the definitions of culture from anthropology and psychology as a
point of departure. We then examine methodologies adopted for creating
cross-cultural datasets, strategies for cultural inclusion in downstream tasks,
and methodologies that have been used for benchmarking cultural awareness in
LLMs. Further, we discuss the ethical implications of cultural alignment, the
role of Human-Computer Interaction in driving cultural inclusion in LLMs, and
the role of cultural alignment in driving social science research. We finally
provide pointers to future research based on our findings about gaps in the
literature.