A framework for annotating and modelling intentions behind metaphor use

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel taxonomy of intentions behind metaphor use has been proposed, comprising 9 distinct categories. This framework addresses a gap in natural language processing (NLP) applications, where no comprehensive taxonomy for metaphor intentions previously existed. Alongside the taxonomy, the first dataset specifically annotated for these intentions has been released. Researchers utilized this new dataset to evaluate large language models (LLMs) on their capability to infer the intentions behind metaphor use. Experiments were conducted in both zero-shot and in-context few-shot settings. The findings indicate that accurately inferring these complex communicative intentions remains a significant challenge for current LLMs, highlighting an area for further development in language understanding.

Key takeaway

For NLP engineers developing advanced language models, this research highlights a critical gap: current LLMs struggle significantly with inferring intentions behind metaphor use. Your efforts should focus on integrating the newly proposed 9-category taxonomy and the released dataset into training and evaluation pipelines. This will be crucial for building models that achieve a more nuanced understanding of human communication, moving beyond literal interpretation to grasp complex communicative intent.

Key insights

A new 9-category taxonomy and dataset show LLMs still struggle to infer intentions behind metaphor use.

Principles

• Metaphors shape world conceptualization.
• Metaphor understanding challenges LMs.
• Intentions behind metaphor are complex.

Method

A 9-category taxonomy of metaphor intentions was developed. A new dataset was annotated and then used to test LLMs' inference capabilities in zero- and in-context few-shot settings.

In practice

• Utilize the 9-category taxonomy.
• Employ the new metaphor intention dataset.

Topics

• Metaphor
• Natural Language Processing
• Large Language Models
• Taxonomy Development
• Dataset Annotation
• Language Understanding

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.