Idioms have long posed a challenge due to their unique linguistic properties, which set them apart from other common expressions. While recent studies have leveraged large language models (LLMs) to handle idioms across various tasks, e.g., idiom-containing sentence generation and idiomatic machine translation, little is known about the underlying mechanisms of idiom processing in LLMs, particularly in multilingual settings. To this end, we introduce MIDAS, a new large-scale dataset of idioms in six languages, each paired with its corresponding meaning. Leveraging this resource, we conduct a comprehensive evaluation of LLMs’ idiom processing ability, identifying key factors that influence their performance. Our findings suggest that LLMs rely not only on memorization, but also adopt a hybrid approach that integrates contextual cues and reasoning, especially when processing compositional idioms. This implies that idiom understanding in LLMs emerges from an interplay between internal knowledge retrieval and reasoning-based inference.

Jujeop is a type of pun and a unique way for fans to express their love for the K-pop stars they follow using Korean. One of the unique characteristics of Jujeop is its use of exaggerated expressions to compliment K-pop stars, which contain or lead to humor. Based on this characteristic, Jujeop can be separated into four distinct types, with their own lexical collocations: (1) Fragmenting words to create a twist, (2) Homophones and homographs, (3) Repetition, and (4) Nonsense. Thus, the current study first defines the concept of Jujeop in Korean, manually labels 8.6K comments and annotates the comments to one of the four Jujeop types. With the given annotated corpus, this study presents distinctive characteristics of Jujeop comments compared to the other comments by classification task. Moreover, with the clustering approach, we proposed a structural dependency within each Jujeop type. We have made our dataset publicly available for future research of Jujeop expressions.