Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

This study investigates Large Language Models' (LLMs) metaphor processing capabilities across three dimensions: concept mapping, metaphor-literal repository, and syntactic sensitivity. Researchers from the University of Macau found that LLMs generate 15%-25% conceptually irrelevant interpretations, often misinterpreting phrases like "fall in love" as "drop down from love." The models primarily rely on inherent metaphorical indicators in their training data, exhibiting a 65%-80% overlap between contextualized and de-contextualized outputs, rather than utilizing contextual cues effectively. Furthermore, LLMs demonstrate greater sensitivity to syntactic irregularities, such as those in POS-shuffled sentences, than to comprehensive structural understanding. These findings, derived from experiments using datasets like Fig-QA and MUNCH with models including GPT-4o, DeepSeek-V3-671B, and LLaMA-3.1-8B, highlight significant limitations in LLMs' deeper metaphorical comprehension.

Key takeaway

For NLP Engineers developing or evaluating LLMs for nuanced language tasks, you should prioritize enhancing conceptual alignment and contextual reasoning in your models. Current LLMs frequently produce conceptually irrelevant interpretations and over-rely on fixed lexical associations, even with novel metaphors. Focus on training methodologies that integrate deeper semantic understanding and robust syntactic processing, moving beyond surface-level pattern matching to truly grasp figurative language. This will improve performance in complex linguistic applications.

Key insights

LLMs struggle with deep metaphor comprehension, often misinterpreting concepts and relying on lexical associations over context or full syntactic understanding.

Principles

• LLMs exhibit 15%-25% concept-irrelevant metaphor interpretations.
• Metaphor-literal repositories drive LLM metaphor processing.
• Syntactic irregularities can be mistaken for metaphorical indicators.

Method

The study employs spatial analysis using high-dimensional embedding projections to quantify conceptual irrelevance, metaphorical imagination to assess context utilization via overlap ratios, and syntactic shuffling to evaluate structural influence on metaphor detection.

In practice

• Avoid multi-choice tasks for robust LLM metaphor evaluation.
• Focus on conceptual alignment beyond lexical matching in LLM training.
• Consider syntactic structure manipulation for metaphor detection benchmarks.

Topics

• Large Language Models
• Metaphor Processing
• Natural Language Understanding
• Conceptual Alignment
• Contextual Reasoning
• Syntactic Analysis

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.