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The Revolution of Multimodal Large Language Models: A Survey
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Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara. Findings of the Association for Computational Linguistics: ACL 2024. 2024.
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“users should be aware that limitations such as introducing hallucination71 and omitting necessary signals73 are inherent to LLMs and may still impact classification performance, though MoMA is fine-tuned using groundtruth clinical labels.”
From MoMA: a mixture-of-multimodal-agents architecture for enhancing clinical prediction modelling (2025) · cites this paper as [73]
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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“COCO (Lin et al., 2014) contains more than 120k images, each associated with five human-generated captions.”
InfluentialLanguage Models are Few-Shot Learners
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“Initially, this breakthrough was lever-aged for language-specific models (Devlin et al., 2018; Brown et al., 2020) but quickly extended to support diverse modalities (Li et al., 2019; Lu et al., 2019) and facilitate their integration within unified embedding spaces (Radford et al., 2021).”
Learning Transferable Visual Models From Natural Language Supervision
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“Initially, this breakthrough was lever-aged for language-specific models (Devlin et al., 2018; Brown et al., 2020) but quickly extended to support diverse modalities (Li et al., 2019; Lu et al., 2019) and facilitate their integration within unified embedding spaces (Radford et al., 2021).”
LoRA: Low-Rank Adaptation of Large Language Models
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“This technique is orthogonal to quantization methods such as QLoRA (Dettmers et al., 2024), which further decreases the memory footprint of the LLM compared to the usual half-precision weights.”
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Industrial Foundation Models (IFMs) for intelligent manufacturing: A systematic review
Zhao, S., Liu, S., Jiang, Y., Zhao, B. M., Lv, Y., Zhang, J., Wang, L., & Zhong, R. Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.06.011 · Cited by 50
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“Employing Multi-modal Large Language Models 163 , next-generation smart glasses not only enhance the quality of real-time decision-making 164 but also deliver personalized suggestions 165 derived from aggregated life and dietary data.”
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Self-citationSafe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 35
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Metadata
- Authors
- Caffagni, D., Cocchi, F., Barsellotti, L., Moratelli, N., Sarto, S., Baraldi, L., Baraldi, L., Cornia, M., & Cucchiara, R.
- Affiliations
- University of Modena and Reggio Emilia (IT)
- University of Pisa (IT)
- ORCID
- Venue
- Findings of the Association for Computational Linguistics ACL 2024
- Published
- 2024
- Pages
- 13590-13618
- OpenAlex
- W4402670262
- Cited by
- 98
- References
- 171
- Citations by year
- 2026: 44 · 2025: 46 · 2024: 8
- FWCI
- 20.26
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- Funders
- European Commission (PNRR-M4C2, PE00000013)
- 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 › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multimodal large language models, multimodal language models, vision-language models, large language models, multimodal representation learning
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