面向社交对话智能体的认知启发组件
Cognitively Inspired Components for Social Conversational Agents
AI总结：
本文综述认知启发组件如何通过语义与情景记忆、情绪、工作记忆和学习能力，同时改善对话智能体的技术缺陷与社会规范遵循问题。
摘要：
近年来，由于 GPT3 等大型语言模型（LLM）的影响，当前对话智能体（CA）在对话质量方面已有提升。然而，仍存在两类关键问题。首先是创建 CA 时所采用方法导致的特有技术问题，例如检索型智能体的作用范围问题，以及早期生成式智能体常常给出荒谬回答的问题。其次，人类将 CA 视为社会行动者，因此期望 CA 遵守社会规范。CA 在这方面的失败可能导致糟糕的交互，甚至让用户感知到威胁。为此，本文提出一项综述，强调通过为 CA 引入认知启发的附加组件来同时解决这两类问题的一种潜在方案。借助语义记忆和情景记忆、情绪、工作记忆以及学习能力的计算化摹本，有可能同时解决 CA 所遇到的技术问题和社会问题。
英文摘要：
Current conversational agents (CA) have seen improvement in conversational quality in recent years due to the influence of large language models (LLMs) like GPT3. However, two key categories of problem remain. Firstly there are the unique technical problems resulting from the approach taken in creating the CA, such as scope with retrieval agents and the often nonsensical answers of former generative agents. Secondly, humans perceive CAs as social actors, and as a result expect the CA to adhere to social convention. Failure on the part of the CA in this respect can lead to a poor interaction and even the perception of threat by the user. As such, this paper presents a survey highlighting a potential solution to both categories of problem through the introduction of cognitively inspired additions to the CA. Through computational facsimiles of semantic and episodic memory, emotion, working memory, and the ability to learn, it is possible to address both the technical and social problems encountered by CAs.
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。
