对多式联运大型语言模型的调查
Shukang Yin1, Chaoyou Fu2,3, Sirui Zhao1
1School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China.
National science review
|December 16, 2024
概括
多模式大语言模型 (MLLMs) 显示出新兴的能力,向人工通用智能迈进. 这篇论文调查了最近的MLLM进展,涵盖了架构,培训和未来的研究方向.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 多模式大语言模型 (MLLMs),以GPT-4V为例,是一个快速发展的研究领域.
- 在复杂的多式联络任务中,MLLM利用大型语言模型 (LLM),展现出超越传统方法的新兴能力.
- 律师学位的发展正在加速,来自学术界和工业界的重大贡献.
研究的目的:
- 提供多模大型语言模型 (MLLMs) 近期进展的全面概述和摘要.
- 概述MLLM的基本表述,架构,培训策略,数据和评估指标.
- 探索MLLM领域内的扩展和挑战,包括多模式,多语言和高级推理技术.
主要方法:
- 系统审查和总结当前的MLLM研究.
- 对MLLM架构,培训方法和数据集的分析.
- 探索新兴研究主题,如增强细分化,模式支持,语言功能和场景应用.
- 调查多模式幻觉和先进的技术,如上下文学习,思维链和LLM辅助的视觉推理.
主要成果:
- 最近的MLLM展示了令人惊的新兴能力,包括基于图像的故事生成和无光学字符识别的数学推理.
- 在开发与GPT-4V.等现有基准竞争或超越的MLLM方面取得了显著进展.
- 该领域正在迅速发展,目前正在努力在各个维度中增强MLLM能力.
结论:
- 由于其新兴的能力,MLLM代表了向人工通用智能迈出的重要一步.
- 本调查提供了对MLLM景观的结构化理解,突出了关键概念和研究轨迹.
- 未来的研究应该解决当前的挑战,并探索有前途的方向,以进一步推进MLLM技术.
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