利用语言模型作为认知代理的知识来源
Exploiting Language Models as a Source of Knowledge for Cognitive Agents
AI总结：
本文探讨利用大型语言模型作为认知代理的外部知识源，识别挑战与机遇，并提出将知识提取与认知架构集成以提升效果的方法。
摘要：
大型语言模型（LLMs）提供的能力远不止句子补全，还包括问答、摘要和自然语言推理。虽然这些能力中的许多都有潜力应用于认知系统，但我们的研究正在利用语言模型作为认知代理（即通过认知架构实现的代理）的任务知识来源。我们识别了将语言模型用作认知系统的外部知识源所面临的挑战和机遇，以及通过将知识提取与认知架构能力相集成来提高知识提取有效性的可能方法，并利用我们最近在该领域的工作中的示例加以说明。
英文摘要：
Large language models (LLMs) provide capabilities far beyond sentence completion, including question answering, summarization, and natural-language inference. While many of these capabilities have potential application to cognitive systems, our research is exploiting language models as a source of task knowledge for cognitive agents, that is, agents realized via a cognitive architecture. We identify challenges and opportunities for using language models as an external knowledge source for cognitive systems and possible ways to improve the effectiveness of knowledge extraction by integrating extraction with cognitive architecture capabilities, highlighting with examples from our recent work in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
