LLM-Generated Norms for 300 English Metaphors Across Five Psycholinguistic Dimensions
by Laura Pissani·Updated 2d ago
145.4 KB1files
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Description
Laura Pissani's study investigates the use of large language models to generate psycholinguistic norms for 300 English two-word metaphor combinations. The dataset, last updated in June 2026, contains model-generated ratings for familiarity, aptness, concreteness, metaphoricity, and constituency. It compares norms generated by eight LLMs under different presentation and response formats against human ratings.
Use Cases
Benchmarking LLM performance on psycholinguistic tasks based on the five normed dimensions.
Analyzing the relationship between word co-occurrence patterns and metaphor ratings based on the described findings.
Comparing the effect of stimulus presentation (in-context vs. isolation) on model-generated norms.
Replicating analyses of metaphor comprehension using automated norm generation.
Strengths
Focuses on 300 specific English metaphor combinations, providing a defined scope.
Evaluates norms across five distinct psycholinguistic dimensions: familiarity, aptness, concreteness, metaphoricity, and constituency.
Compares outputs from eight different large language models.
Uses a Creative Commons CC-BY-4.0 license for open sharing.
Limitations
Dataset scale is limited to 145.4 KB, suggesting a small number of records.
Column-level documentation is absent; field semantics must be inferred from the description.
Row count is unknown, which may limit suitability assessment.
Provenance
Source
Laura Pissani
Collection Method
Generated by prompting eight large language models to norm metaphor combinations.
Freshness
Last updated 2026-06-04 04:27:29; freshness should be verified.
Primary data file is a PDF; data extraction may be required for computational use.