  While machines learn from existing corpora, humans have the unique capability
to establish and accept new language systems. This makes human form unique
language systems within social groups. Aligning with this, we focus on a gap
remaining in addressing translation challenges within social groups, where
in-group members utilize unique terminologies. We propose KpopMT dataset, which
aims to fill this gap by enabling precise terminology translation, choosing
Kpop fandom as an initiative for social groups given its global popularity.
Expert translators provide 1k English translations for Korean posts and
comments, each annotated with specific terminology within social groups'
language systems. We evaluate existing translation systems including GPT models
on KpopMT to identify their failure cases. Results show overall low scores,
underscoring the challenges of reflecting group-specific terminologies and
styles in translation. We make KpopMT publicly available.
