  To translate well, machine translation (MT) systems and general-purposed
language models (LMs) need a deep understanding of both source and target
languages and cultures. Therefore, idioms, with their non-compositional nature,
pose particular challenges for Transformer-based systems, as literal
translations often miss the intended meaning. Traditional methods, which
replace idioms using existing knowledge bases (KBs), often lack scale and
context awareness. Addressing these challenges, our approach prioritizes
context awareness and scalability, allowing for offline storage of idioms in a
manageable KB size. This ensures efficient serving with smaller models and
provides a more comprehensive understanding of idiomatic expressions. We
introduce a multilingual idiom KB (IdiomKB) developed using large LMs to
address this. This KB facilitates better translation by smaller models, such as
BLOOMZ (7.1B), Alpaca (7B), and InstructGPT (6.7B), by retrieving idioms'
figurative meanings. We present a novel, GPT-4-powered metric for human-aligned
evaluation, demonstrating that IdiomKB considerably boosts model performance.
Human evaluations further validate our KB's quality.
