Self-Improvement in Multimodal Large Language Models: A Survey
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908574348738560 |
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| author | Deng, Shijian Wang, Kai Yang, Tianyu Singh, Harsh Tian, Yapeng |
| author_facet | Deng, Shijian Wang, Kai Yang, Tianyu Singh, Harsh Tian, Yapeng |
| contents | Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02665 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Improvement in Multimodal Large Language Models: A Survey Deng, Shijian Wang, Kai Yang, Tianyu Singh, Harsh Tian, Yapeng Computation and Language Recent advancements in self-improvement for Large Language Models (LLMs) have efficiently enhanced model capabilities without significantly increasing costs, particularly in terms of human effort. While this area is still relatively young, its extension to the multimodal domain holds immense potential for leveraging diverse data sources and developing more general self-improving models. This survey is the first to provide a comprehensive overview of self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview of the current literature and discuss methods from three perspectives: 1) data collection, 2) data organization, and 3) model optimization, to facilitate the further development of self-improvement in MLLMs. We also include commonly used evaluations and downstream applications. Finally, we conclude by outlining open challenges and future research directions. |
| title | Self-Improvement in Multimodal Large Language Models: A Survey |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.02665 |