通过对LLM解释的趋同条件来加速人机交互
Aleksandr Raikov1, Alberto Giretti2, Massimiliano Pirani3
1Jinan Institute of Supercomputing Technology, Jinan, Shandong, China.
Frontiers in artificial intelligence
|June 17, 2024
概括
这项研究使用大语言模型 (LLM) 和可解释的人工智能 (XAI) 增强了人机交互. 一种新的融合方法加速了决策者在战略规划中的集体问题解决.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 人与计算机的交互
背景情况:
- 传统的可解释的人工智能 (XAI) 范式与大型语言模型 (LLM) 的细微差别作斗争.
- 人机交互正在迅速发展,需要先进的AI解释能力.
- 在AI系统中,LLM引入了复杂的认知和语义元素.
研究的目的:
- 开发一种方法来加速人机交互,使用LLMs.
- 通过结合认知解释,将XAI扩展到传统的逻辑框架之外.
- 在混合人机环境中确保有目的和可持续的XAI.
主要方法:
- 一种融合的方法,整合了反向问题解决,认知建模,遗传算法,神经网络,因果循环动力学和自身形式实现.
- 分析LLM的可形式化不良的认知语义解释.
- 在情境中心的集体战略规划场景中实施.
主要成果:
- 通过LLM集成,证明了集体解决问题的趋同的加速.
- 在数字化的人机交互中建立有目的和可持续的XAI条件.
- 在现实世界的战略规划环境中成功应用.
结论:
- 决策者可以利用LLM来为信息流程创造独特的结构条件.
- 拟议的方法提高了XAI在复杂问题解决方面的有效性.
- 这些发现支持在各种经济,科学和技术领域推进可解释的LLM.
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