数据与多模式大语言模型之间的协同作用:从共同开发的角度进行的一项调查.
概括
多模式大型语言模型 (MLLMs) 的开发及其数据相互连接. 本综述探讨了以数据为中心的方法如何增强MLLM的能力,以及MLLM如何为数据开发做出贡献.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 已经迅速发展,多模式LLM (MLLMs) 扩展了文本之外的功能.
- 许多MLLM需要大量的参数和数据,这凸显了数据在其发展中的越来越重要.
- 在MLLM开发和数据之间的关系越来越被认为是相互关联的.
研究的目的:
- 从数据模型共同开发的角度系统地审查MLLM的现有工作.
- 阐明以数据为中心的方法如何在不同开发阶段增强MLLM能力.
- 了解MLLM可以在为多模式数据开发做出贡献方面发挥的作用.
主要方法:
- 系统的文献审查最近的数据驱动的作品在多模式的大型语言模型.
- 分析MLLM开发阶段与以数据为中心的策略之间的相互作用.
- 多模式数据创建和增强的MLLM贡献的分类.
主要成果:
- 通过更广泛和更高质量的数据,MLLM的性能直接得到改善.
- 多模式数据库主动促进多模式数据集的开发和完善.
- 对数据模型共同开发的清晰理解对于推动MLLM研究至关重要.
结论:
- 共同开发MLLM和多模式数据对于释放新兴能力至关重要.
- 在每个MLLM开发阶段确定最佳的以数据为中心的策略是关键.
- 多模式数据管理系统可以作为生成和改进多模式数据的强大工具,创建一个协同循环.
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