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MM-LLMs: Recent Advances in MultiModal Large Language Models

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Zhang, Duzhen, Yu, Yahan, Dong, Jiahua, Li, Chenxing, Su, Dan, Chu, Chenhui, Yu, Dong·2024·Findings of the Association for Computational Linguistics ACL 2024
DOI 10.18653/v1/2024.findings-acl.738
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Abstract

In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks.In this paper, we provide a comprehensive survey aimed at facilitating further research on MM-LLMs.Initially, we outline general design formulations for model architecture and training pipeline.Subsequently, we introduce a taxonomy encompassing 126 MM-LLMs, each characterized by its specific formulations.Furthermore, we review the performance of selected MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs.Finally, we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website 1 for the latest developments in the field.We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.

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Metadata

Authors
Zhang, D., Yu, Y., Dong, J., Li, C., Su, D., Chu, C., & Yu, D.
Affiliations
  • Tencent (China) (CN)
  • Kyoto University (JP)
  • Mohamed bin Zayed University of Artificial Intelligence (AE)
ORCID
Venue
Findings of the Association for Computational Linguistics ACL 2024
Publisher
Association for Computational Linguistics
Published
2024
Pages
12401-12430
DOI
10.18653/v1/2024.findings-acl.738
OpenAlex
W4402670135
Cited by
260
References
0
Citations by year
2026: 69 · 2025: 142 · 2024: 49
FWCI
53.74
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Japan Society for the Promotion of Science (23K28144)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
multimodal large language models, model architecture design, training pipeline, benchmark performance, low-cost training

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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