Probing Multimodal Large Language Models for Global and Local Semantic Representations

Mingxu Tao, Quzhe Huang, Kun Xu, Liwei Chen, Yansong Feng, Dongyan Zhao

Abstract
The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works leverage image-caption datasets to train MLLMs, achieving state-of-the-art performance on image-to-text tasks. However, there are few studies exploring which layers of MLLMs make the most effort to the global image information, which plays vital roles in multimodal comprehension and generation. In this study, we find that the intermediate layers of models can encode more global semantic information, whose representation vectors perform better on visual-language entailment tasks, rather than the topmost layers. We further probe models regarding local semantic representations through object recognition tasks. We find that the topmost layers may excessively focus on local information, leading to a diminished ability to encode global information. Our code and data are released via https://github.com/kobayashikanna01/probing_MLLM_rep.
Anthology ID:
2024.lrec-main.1142
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
13050–13056
Language:
URL:
https://aclanthology.org/2024.lrec-main.1142
DOI:
Bibkey:
Cite (ACL):
Mingxu Tao, Quzhe Huang, Kun Xu, Liwei Chen, Yansong Feng, and Dongyan Zhao. 2024. Probing Multimodal Large Language Models for Global and Local Semantic Representations. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13050–13056, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Probing Multimodal Large Language Models for Global and Local Semantic Representations (Tao et al., LREC-COLING 2024)
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https://preview.aclanthology.org/add_acl24_videos/2024.lrec-main.1142.pdf
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