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| Autores principales: | , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2409.18991 |
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| _version_ | 1866917129616359424 |
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| author | Li, Ming Chen, Keyu Bi, Ziqian Liu, Ming Song, Xinyuan Jiang, Zekun Wang, Tianyang Peng, Benji Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Pan, Xuanhe Xu, Jiawei Feng, Pohsun |
| author_facet | Li, Ming Chen, Keyu Bi, Ziqian Liu, Ming Song, Xinyuan Jiang, Zekun Wang, Tianyang Peng, Benji Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Pan, Xuanhe Xu, Jiawei Feng, Pohsun |
| contents | 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 video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Surveying the MLLM Landscape: A Meta-Review of Current Surveys Li, Ming Chen, Keyu Bi, Ziqian Liu, Ming Song, Xinyuan Jiang, Zekun Wang, Tianyang Peng, Benji Niu, Qian Liu, Junyu Wang, Jinlang Zhang, Sen Pan, Xuanhe Xu, Jiawei Feng, Pohsun Computation and Language 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 video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field. |
| title | Surveying the MLLM Landscape: A Meta-Review of Current Surveys |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.18991 |