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Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and…

Computation and Language · Computer Science 2024-09-09 Jian Li , Weiheng Lu , Hao Fei , Meng Luo , Ming Dai , Min Xia , Yizhang Jin , Zhenye Gan , Ding Qi , Chaoyou Fu , Ying Tai , Wankou Yang , Yabiao Wang , Chengjie Wang

Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties; yet, dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient…

Computer Vision and Pattern Recognition · Computer Science 2025-12-01 Jing Hao , Yuci Liang , Lizhuo Lin , Yuxuan Fan , Wenkai Zhou , Kaixin Guo , Zanting Ye , Yanpeng Sun , Xinyu Zhang , Yanqi Yang , Qiankun Li , Hao Tang , James Kit-Hon Tsoi , Linlin Shen , Kuo Feng Hung

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have…

Artificial Intelligence · Computer Science 2024-09-30 Lin Li , Guikun Chen , Hanrong Shi , Jun Xiao , Long Chen

Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utterances. Despite its significance, little research has…

Computation and Language · Computer Science 2025-04-25 Hanlei Zhang , Zhuohang Li , Yeshuang Zhu , Hua Xu , Peiwu Wang , Haige Zhu , Jie Zhou , Jinchao Zhang

Multimodal large language models (MLLMs) have shown great potential in perception and interpretation tasks, but their capabilities in predictive reasoning remain under-explored. To address this gap, we introduce a novel benchmark that…

Computer Vision and Pattern Recognition · Computer Science 2023-10-23 Mingwei Zhu , Leigang Sha , Yu Shu , Kangjia Zhao , Tiancheng Zhao , Jianwei Yin

Recent advances in large vision-language models (LVLMs) have demonstrated strong performance on general-purpose medical tasks. However, their effectiveness in specialized domains such as dentistry remains underexplored. In particular,…

Computer Vision and Pattern Recognition · Computer Science 2025-09-12 Jing Hao , Yuxuan Fan , Yanpeng Sun , Kaixin Guo , Lizhuo Lin , Jinrong Yang , Qi Yong H. Ai , Lun M. Wong , Hao Tang , Kuo Feng Hung

Reliable interpretation of multimodal data in dentistry is essential for automated oral healthcare, yet current multimodal large language models (MLLMs) struggle to capture fine-grained dental visual details and lack sufficient reasoning…

Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries without considering user experiences, inadequately…

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., vision, audio), positioning LLMs as the "brain" and various…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Jiaxing Huang , Jingyi Zhang

Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, current benchmarks for measuring the intelligence of MLLMs…

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Chaoyou Fu , Yi-Fan Zhang , Shukang Yin , Bo Li , Xinyu Fang , Sirui Zhao , Haodong Duan , Xing Sun , Ziwei Liu , Liang Wang , Caifeng Shan , Ran He

With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark…

MLLMs have recently become a focal point in the field of artificial intelligence research. Building on the strong capabilities of LLMs, MLLMs are adept at addressing complex multi-modal tasks. With the release of GPT-4, MLLMs have gained…

Machine Learning · Computer Science 2025-05-01 Jiarui Ye , Hao Tang

While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplored, particularly in dentistry. Here we introduce…

Multimodal Large Language Models (MLLMs) demonstrate impressive problem-solving abilities across a wide range of tasks and domains. However, their capacity for face understanding has not been systematically studied. To address this gap, we…

Computer Vision and Pattern Recognition · Computer Science 2026-01-21 Kartik Narayan , Vibashan VS , Vishal M. Patel

This paper provides a comprehensive survey of the latest research on multilingual large language models (MLLMs). MLLMs not only are able to understand and generate language across linguistic boundaries, but also represent an important…

Computation and Language · Computer Science 2024-11-20 Shaolin Zhu , Supryadi , Shaoyang Xu , Haoran Sun , Leiyu Pan , Menglong Cui , Jiangcun Du , Renren Jin , António Branco , Deyi Xiong

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data…

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and…

Computation and Language · Computer Science 2025-12-09 Ming Li , Keyu Chen , Ziqian Bi , Ming Liu , Xinyuan Song , Zekun Jiang , Tianyang Wang , Benji Peng , Qian Niu , Junyu Liu , Jinlang Wang , Sen Zhang , Xuanhe Pan , Jiawei Xu , Pohsun Feng

In recent years, large language models (LLMs) have demonstrated remarkable potential across various medical applications. Building on this foundation, multimodal large language models (MLLMs) integrate LLMs with visual models to process…

Computation and Language · Computer Science 2025-03-11 Xiaoyi Liang , Mouxiao Bian , Moxin Chen , Lihao Liu , Junjun He , Jie Xu , Lin Li

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large…

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