Measuring the Contributions of Vision and Text Modalities

Letitia Parcalabescu


Abstract
This dissertation investigates multimodal transformers that process both image and text modalities together to generate outputs for various tasks (such as answering questions about images). Specifically, methods are developed to assess the effectiveness of vision and language models in combining, understanding, utilizing, and explaining information from these two modalities. The dissertation contributes to the advancement of the field in three ways: (i) by measuring specific and task-independent capabilities of vision and language models, (ii) by interpreting these models to quantify the extent to which they use and integrate information from both modalities, and (iii) by evaluating their ability to provide self-consistent explanations of their outputs to users.
Anthology ID:
2025.jlcl-1.1
Volume:
Journal for Language Technology and Computational Linguistics, Vol. 38 No. 1
Month:
February
Year:
2025
Address:
Editor:
Christian Wartena
Venue:
JLCL
SIG:
Publisher:
German Society for Computational Linguistics and Language Technology
Note:
Pages:
1–3
Language:
URL:
https://aclanthology.org/2025.jlcl-1.1/
DOI:
10.21248/jlcl.38.2025.261
Bibkey:
Cite (ACL):
Letitia Parcalabescu. 2025. Measuring the Contributions of Vision and Text Modalities. Journal for Language Technology and Computational Linguistics, 38(1):1–3.
Cite (Informal):
Measuring the Contributions of Vision and Text Modalities (Parcalabescu, JLCL 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.jlcl-1.1.pdf