Memorization or Reasoning? Exploring the Idiom Understanding of LLMs

• URL: http://arxiv.org/abs/2505.16216v1
• Date: Thu, 22 May 2025 04:31:25 GMT
• Title: Memorization or Reasoning? Exploring the Idiom Understanding of LLMs
• Authors: Jisu Kim, Youngwoo Shin, Uiji Hwang, Jihun Choi, Richeng Xuan, Taeuk Kim,
• Abstract summary: MIDAS is a large-scale dataset of idioms in six languages, each paired with its corresponding meaning.<n>Our findings suggest that LLMs rely not only on memorization, but also adopt a hybrid approach that integrates contextual cues and reasoning.<n>This implies that idiom understanding in LLMs emerges from an interplay between internal knowledge retrieval and reasoning-based inference.
• Score: 6.046971695786252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
  Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
  arXiv  Detail & Related papers  (2025-06-11T16:00:54Z)
• Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models [49.16690802656554]
  We find that Multilingual factual models struggle to provide consistent responses to semantically equivalent prompts in different languages.<n>We propose a linear shortcut method that bypasses computations in the final layers, enhancing both prediction accuracy and cross-lingual consistency.
  arXiv  Detail & Related papers  (2025-04-05T19:43:10Z)
• Linguistic Blind Spots of Large Language Models [14.755831733659699]
  We study the performance of recent large language models (LLMs) on linguistic annotation tasks.<n>We find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs.<n>Our results provide insights to inform future advancements in LLM design and development.
  arXiv  Detail & Related papers  (2025-03-25T01:47:13Z)
• Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context [12.781022584125925]
  We construct a novel, controlled contrastive dataset designed to test whether LLMs can effectively use context to disambiguate idiomatic meaning.<n>Our findings reveal that LLMs often fail to resolve idiomaticity when it is required to attend to the surrounding context.<n>We make our code and dataset publicly available.
  arXiv  Detail & Related papers  (2024-10-21T14:47:37Z)
• How Do Multilingual Language Models Remember Facts? [50.13632788453612]
  We show that previously identified recall mechanisms in English largely apply to multilingual contexts.<n>We localize the role of language during recall, finding that subject enrichment is language-independent.<n>In decoder-only LLMs, FVs compose these two pieces of information in two separate stages.
  arXiv  Detail & Related papers  (2024-10-18T11:39:34Z)
• LLMs' Understanding of Natural Language Revealed [0.0]
  Large language models (LLMs) are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. We will focus on testing LLMs for their language understanding capabilities, their supposed forte.
  arXiv  Detail & Related papers  (2024-07-29T01:21:11Z)
• MindMerger: Efficient Boosting LLM Reasoning in non-English Languages [26.334092384176518]
  Reasoning capabilities are crucial for Large Language Models (LLMs) We propose MindMerger, which merges LLMs with the external language understanding capabilities from multilingual models. MindMerger consistently outperforms all baselines, especially in low-resource languages.
  arXiv  Detail & Related papers  (2024-05-27T17:41:54Z)
• Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL [78.80673954827773]
  Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias. We propose using Semantic Role Labeling (SRL) as a fundamental task to explore LLMs' ability to extract structured semantics. We find interesting potential: LLMs can indeed capture semantic structures, and scaling-up doesn't always mirror potential. We are surprised to discover that significant overlap in the errors is made by both LLMs and untrained humans, accounting for almost 30% of all errors.
  arXiv  Detail & Related papers  (2024-05-10T11:44:05Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models [117.20416338476856]
  Large language models (LLMs) demonstrate remarkable multilingual capabilities without being pre-trained on specially curated multilingual parallel corpora. We propose a novel detection method, language activation probability entropy (LAPE), to identify language-specific neurons within LLMs. Our findings indicate that LLMs' proficiency in processing a particular language is predominantly due to a small subset of neurons.
  arXiv  Detail & Related papers  (2024-02-26T09:36:05Z)
• Limits for Learning with Language Models [4.20859414811553]
  We show that large language models (LLMs) are unable to learn concepts beyond the first level of the Borel Hierarchy. LLMs will continue to operate without formal guarantees on tasks that require entailments and deep linguistic understanding.
  arXiv  Detail & Related papers  (2023-06-21T12:11:31Z)
• Shortcut Learning of Large Language Models in Natural Language Understanding [119.45683008451698]
  Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. They might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness.
  arXiv  Detail & Related papers  (2022-08-25T03:51:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.