多式联网大型语言模型是否能实现对世界的深入理解?
Igor Farkaš1, Michal Vavrečka1,2, Stefan Wermter3
1Department of Applied Informatics, Comenius University Bratislava, Bratislava, Slovakia.
Frontiers in systems neuroscience
|December 8, 2025
概括
与嵌入式代理集成的多式大型语言模型 (MLLMs) 对符号接地显示出希望. 然而,与发育性学习相比,他们的随机学习阻碍了与人类类似的深度理解.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 大型语言模型 (LLM) 面临符号接地问题,作为没有真正理解的统计模型.
- 多模式LLM (MLLMs) 将语言与视觉或行动整合在一起,以弥合这一差距,从而有可能实现符号接地.
- 与物理世界互动的有形代理是MLLM实现基础理解的关键.
研究的目的:
- 探索MLLM与体内代理商的程度,可以通过现实世界的互动实现接地理解.
- 调查缩小符号,神经表征和体现经验之间的差距的要求.
- 分析当前MLLM在实现类似人类语义理解方面的局限性.
主要方法:
- 整合MLLM与实体化代理进行真实世界的互动.
- 分析MLLM与人类认知发展的发展轨迹.
- 评估随机数据集培训与渐进式结构化学习之间的影响.
主要成果:
- 与嵌入式代理集成的MLLM为符号接地提供了潜在的途径.
- 实现有基础的理解需要在现实环境中整合持续的感官数据,目标导向的行为和适应性学习.
- 由于随机学习轨迹偏离了人类认知发展,目前的MLLM缺乏类似人类的深度理解.
结论:
- 在MLLM中,深层次的,类似人类的理解受到非发展性的随机学习方法的阻碍.
- 结构化,简单到复杂的概念架构对于建立基础知识至关重要.
- 未来的MLLM开发必须考虑发展集成,以提高语义理解和符号接地.
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