arXiv — NLP / Computation & Language · · 3 min read

Can Language Models Imagine Without Seeing? Ekphrasis: Measuring Visual Creative Ideation in Text-Only LLMs

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Computer Science > Computation and Language

arXiv:2608.06967 (cs)
[Submitted on 7 Aug 2026]

Title:Can Language Models Imagine Without Seeing? Ekphrasis: Measuring Visual Creative Ideation in Text-Only LLMs

View a PDF of the paper titled Can Language Models Imagine Without Seeing? Ekphrasis: Measuring Visual Creative Ideation in Text-Only LLMs, by Hongyu Luo and 8 other authors
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Abstract:Current evaluations do not isolate whether text-only language models can originate visual concepts before image generation. Fluent visual prose can hide visual-plan failures: an answer may appear creative while repeating familiar visual clichés or failing to specify a renderable scene. We define Visual Creative Ideation (VCI) as the ability to produce textual visual plans that are useful, expressive, and population-novel, and introduce Ekphrasis, a 400-task benchmark spanning Abstraction, Combination, Transformation, and Adaptation. Ekphrasis scores anonymized pairwise comparisons with dimension-specific checklists, aggregates preferences with Bradley-Terry models, and uses Typed Idea Graphs to convert task-specific population clichés into novelty references. Across 14 language models, VCI separates usefulness, expressiveness, and novelty rather than reducing to fluency: strong models achieve similar overall scores through different profiles, and useful plans can remain visually clichéd. A cross-modal grounding study further shows that text-level VCI ordering largely survives faithful rendering and blind image-level preference judgment, supporting Ekphrasis as a measure of visual ideation beyond prose quality.
Comments: 25 pages, 4 main figures, with appendices. Code and data: this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.06967 [cs.CL]
  (or arXiv:2608.06967v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.06967
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hongyu Luo [view email]
[v1] Fri, 7 Aug 2026 08:44:00 UTC (2,600 KB)
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