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The Revolution of Multimodal Large Language Models: A Survey [PDF]

Findings of the Association for Computational Linguistics ACL 2024
ACL 2024 (Findings)
Davide Caffagni, Federico Cocchi, Luca Barsellotti, Nicholas Moratelli, Sara Sarto, Lorenzo Baraldi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara   +8 more
core   +9 more sources

Personalized Multimodal Large Language Models: A Survey

CoRR
Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities, such as text, images, and audio, to perform complex tasks with high accuracy. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on ...
Junda Wu, Hanjia Lyu, Yu Xia 0007, Zhehao Zhang 0001, Joe Barrow, Ishita Kumar, Mehrnoosh Mirtaheri, Hongjie Chen 0003, Ryan A. Rossi, Franck Dernoncourt, Tong Yu 0001, Ruiyi Zhang 0002, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang 0160, Xiang Chen 0010, Hanieh Deilamsalehy, Namyong Park 0001, Sungchul Kim, Huanrui Yang, Subrata Mitra, Zhengmian Hu, Nedim Lipka, Dang Nguyen, Yue Zhao 0016, Jiebo Luo 0001, Julian J. McAuley   +26 more
core   +5 more sources

Efficient multimodal large language models: a survey

Visual Intelligence
In the past years, multimodal large language models (MLLMs) have demonstrated remarkable performance in tasks such as visual question answering and visual understanding and reasoning.
Yizhang Jin, Jian Li, Tianjun Gu, Yexin Liu, Bo Zhao, Jinxiang Lai, Zhenye Gan, Yabiao Wang, Chengjie Wang, Xin Tan, Lizhuang Ma   +10 more
doaj   +4 more sources

Leveraging Large Language Models for Multimodal Search

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Published at CVPRW ...
Barbany Mayor, Oriol, Huang, Michael, Zhu, Xinliang, Dhua, Arnab   +3 more
openaire   +5 more sources

Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation

Proceedings of the AAAI Conference on Artificial Intelligence
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs). Most existing studies have focused on converting user behavior logs into textual prompts and leveraging techniques such as prompt tuning to enable LLMs for recommendation tasks. Meanwhile, research interest has recently
Yuyang Ye 0002, Zhi Zheng 0008, Yishan Shen, Tianshu Wang 0004, Hengruo Zhang, Peijun Zhu, Runlong Yu, Kai Zhang 0001, Hui Xiong 0001   +8 more
core   +10 more sources

LaVy: Vietnamese Multimodal Large Language Model [PDF]

CoRR
Large Language Models (LLMs) and Multimodal Large language models (MLLMs) have taken the world by storm with impressive abilities in complex reasoning and linguistic comprehension. Meanwhile there are plethora of works related to Vietnamese Large Language Models, the lack of high-quality resources in multimodality limits the progress of Vietnamese ...
Chi Tran, Huong Le Thanh
core   +5 more sources

Large language models and multimodal foundation models for precision oncology

npj Precision Oncology
The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research.
Daniel Truhn, Jan-Niklas Eckardt, Dyke Ferber, Jakob Nikolas Kather   +3 more
doaj   +4 more sources

The application of multimodal large language models in medicine

The Lancet Regional Health. Western Pacific
Jianing Qiu, Wu Yuan, Kyle Lam
doaj   +3 more sources

Multimodal Large Language Model for Visual Navigation

CoRR, 2023
Recent efforts to enable visual navigation using large language models have mainly focused on developing complex prompt systems. These systems incorporate instructions, observations, and history into massive text prompts, which are then combined with pre-trained large language models to facilitate visual navigation.
Yao-Hung Hubert Tsai, Vansh Dhar, Jialu Li, Bowen Zhang, Jian Zhang 0050   +4 more
openaire   +3 more sources

Embodiment in multimodal large language models

Neuron
Multimodal Large Language Models (MLLMs) have demonstrated extraordinary progress in bridging textual and visual inputs. However, MLLMs still face challenges in situated physical and social interactions in sensorally rich, multimodal and real-world settings where the embodied experience of the living organism is essential.
Kadambi, Akila, Aziz-Zadeh, Lisa, Damasio, Antonio, Iacoboni, Marco, Narayanan, Srini   +4 more
openaire   +3 more sources