Memorization or Reasoning? Exploring the Idiom Understanding of LLMs
Jisu Kim, Youngwoo Shin, Uiji Hwang, Jihun Choi, Richeng Xuan, Taeuk Kim
Abstract
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 MI-DAS, 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.Datasets # Instances (Language) Meaning ID10M 4,568 (EN), 1,301 (ZH) 1,229 (ES), 189 (NL), 188 (FR), 819 (DE), 452 (IT), 165 (JA), 648 (PL), 559 (PT) LIdioms 291 (EN), 114 (PT), 175 (IT), 130 (DE), 105 (RU)
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Cited by top-tier papers2
- Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource LanguagesSaeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina et al.ACL 2026
- EIFFEL: a novel benchmark to measure bias of English heavy training on French idiomatic expressionsCharlotte Noel, Nicholas Asher, Olivier Gouvert, Farah Benamara et al.ACL 2026
Builds on6
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- Measuring Massive Multitask Language UnderstandingDan Hendrycks, Collin Burns, Steven Basart, Andy Zou et al.ICLR 2021 · 7,905 citations
- Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language ModelsShuang Li, Jiangjie Chen, Siyu Yuan, Xinyi Wu et al.AAAI 2024 · 44 citations
- MMTEB: Massive Multilingual Text Embedding BenchmarkKenneth C. Enevoldsen, Isaac Chung, Imene Kerboua, Márton Kardos et al.ICLR 2025 · 10 citations
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