把它留给大型语言模型! 用内存集成进行校正和规划
Yuan Zhang1,2, Chao Wang1,2, Juntong Qi1,2
1School of Future Technology, Shanghai University, Shanghai, China.
Cyborg and bionic systems (Washington, D.C.)
|March 29, 2024
概括
本研究介绍了CPMI,一种新的LLM中心方法,用于实现AI任务,如视觉语言导航. 通过使动态规划和执行成为可能,CPMI提高了代理商的性能,提高了在少数情况下的成功率和效率.
科学领域:
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
- 自然语言处理自然语言处理.
背景情况:
- 传统的视觉语言导航 (VLN) 依赖于昂贵的手动标签,用于新环境中的任务规划.
- 大型语言模型 (LLM) 提供了常识知识,但由于障碍和状态变化,在体现的任务中难以动态执行.
- 在VLN中单一平面的方法是脆弱的,容易因轻微的执行错误而失败.
研究的目的:
- 开发一种更有效,更具成本效益的方法,使代理人能够理解和执行体现任务中的自然语言指令.
- 为了利用LLM的推理能力,在任务执行过程中进行动态规划和适应.
- 为了提高代理商的性能和成功率,在VLN的短暂学习场景中进行VLN.
主要方法:
- 提出了一种名为 Correction and Planning with Memory Integration (CPMI) 的新方法,以大型语言模型 (LLM) 为中心.
- 在LLM中心的规划器中集成了辅助模块,以提供记忆和通用体验机制.
- 基于环境反,在任务执行期间促进了动态规划和实时调整.
主要成果:
- 在公开数据集上,CPMI在一些射击学习场景中表现出卓越的表现.
- 这种方法显著提高了任务完成的效率.
- 执行自然语言指令的代理人的成功率大幅增加.
结论:
- 通过使动态规划和适应,CPMI方法有效地提高了LLM对体现任务的能力.
- 记忆和通用体验机制对于提高执行期间的代理性能至关重要.
- 对于更强大,更有效的体内人工智能代理来说,CPMI提供了一个有希望的方向,特别是在低数据制度中.
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