Lecture image placeholder

Premium content

Access to this content requires a subscription. You must be a premium user to view this content.

Monthly subscription - $9.99Pay per view - $4.99Access through your institutionLogin with Underline account
Need help?
Contact us
Lecture placeholder background

workshop paper

ACL 2024

August 15, 2024

Bangkok, Thailand

Evaluating Semantic Relations in Predicting Textual Labels for Images of Abstract and Concrete Concepts

keywords:

vlm

abstractness

concreteness

semantic relations

multi-modal

psycholinguistics

This study investigates the performance of SigLIP, a state-of-the-art Vision-Language Model (VLM), in predicting labels for images depicting 1,278 concepts. Our analysis across 300 images per concept shows that the model frequently predicts the exact user-tagged labels, but similarly, it often predicts labels that are semantically related to the exact labels in various ways: synonyms, hypernyms, co-hyponyms, and associated words, particularly for abstract concepts. We then zoom into the diversity of the user tags of images and word associations for abstract versus concrete concepts. Surprisingly, not only abstract but also concrete concepts exhibit significant variability, thus challenging the traditional view that representations of concrete concepts are less diverse.

Next from ACL 2024

Diachronic change in verb usage statistics predicts differences in sentence processing across the lifespan
workshop paper

Diachronic change in verb usage statistics predicts differences in sentence processing across the lifespan

ACL 2024

Ellis Cain
Ellis Cain and 1 other author

15 August 2024

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)

PRESENTATIONS

  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved