| Challenge: | Static concreteness ratings are widely used in NLP, yet a word’s concreteness can shift with context, especially in figurative language such as metaphor, where common concrete nouns can take abstract interpretations. |
| Approach: | They conduct a layer-wise and geometric analysis of LLM hidden representations across four model families to examine how models distinguish literal vs. figurative usage. |
| Outcome: | The results show that LLMs separate literal and figurative usage in early layers and that mid-to-late layers compress concreteness into a one-dimensional direction consistent across models. |
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| Challenge: | a long tradition in cognitive science treats concreteness as a graded dimension of conceptual representation . concrete words benefit from richer sensory codes and exhibit robust behavioral advantages over abstract words . |
| Approach: | They compare vision-language models with text-only large language models to test their concreteness . they find that VLMs show more human-like sensitivity to concreteness than LLMs . |
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LMs stand their Ground: Investigating the Effect of Embodiment in Figurative Language Interpretation by Language Models (2023.findings-acl)
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| Challenge: | Figures are based on the use of words in a way that deviates from their conventional order and meaning. |
| Approach: | They propose to use a figurative language model to interpret embodied metaphors by using larger language models that conceptualise embodies the action of the metaphorical sentence. |
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Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs (2023.findings-emnlp)
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| Challenge: | Existing studies show that color perception and color language are suitable for empirically studying the problem. |
| Approach: | They propose to quantify alignment between a defined color space and a feature space in a language model by learning a mapping between embedding space and color space. |
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Concreteness vs. Abstractness: A Selectional Preference Perspective (2022.aacl-srw)
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| Challenge: | Using a collection of 5,438 nouns and 1,275 verbs, we exploit selectional preferences as a salient characteristic in classifying abstract vs. concrete words. |
| Approach: | They propose to use selectional preferences as a criterion to distinguish between concrete and abstract concepts and words. |
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Is a cute puyfred cute? Context-dependent form-meaning systematicity in LLMs (2025.findings-acl)
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| Challenge: | valence is encoded in meaningful ways in large language models and in some LLMs, pseudowords affect the representation of whole sentences similarly to words. |
| Approach: | They investigate how LLMs represent valence, a key semantic attribute, and how they deal with contextualisation of pseudowords in sentences. |
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Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding (2025.findings-acl)
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| Challenge: | Existing studies on metaphor processing have focused on single datasets and specific task settings, often using artificially constructed data through lexical replacement. |
| Approach: | They propose to evaluate the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations. |
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LLM-based Literal Example Generation for Japanese Multiword Expressions (2026.acl-srw)
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| Challenge: | Existing work on Japanese multiword expressions has focused on detecting idiomatic usages in context, leaving literal readings underrepresented. |
| Approach: | They propose to use corpus non-literal usages as contrastive cues for controlled prompting . they compare their results to a test that compares model predictions with human judgments . |
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Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets (N18-1)
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| Challenge: | Existing work suggests that concepts with concrete visual manifestations are easier to learn than abstract ones. |
| Approach: | They propose an algorithm for automatically computing the visual concreteness of words and topics within multimodal datasets. |
| Outcome: | The proposed algorithm predicts the capacity of machine learning algorithms to learn textual/visual relationships. |
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions (2025.emnlp-main)
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Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba, Edward Raff, Ponnurangam Kumaraguru, Francis Ferraro, Manas Gaur
| Challenge: | Exact label definitions are considered as clues to disambiguate unclear labels, helping models perform their tasks more effectively. |
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| Outcome: | The results suggest that models often default to internal representations, particularly in general tasks, while domain-specific tasks benefit more from explicit definitions. |
The Interplay between Metaphors and NLP (2026.acl-tutorials)
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| Challenge: | This tutorial will provide an overview of the metaphor processing field. |
| Approach: | This tutorial will provide an overview of the metaphor processing field . it will focus on recent directions opened by LLMs for metaphor interpretation . |
| Outcome: | The tutorial will discuss the influence of various metaphor theories on the creation of annotated resources and models. |