关于聊天机器人在问题解决驱动对话中如何工作的一些假设：大型语言模型作为创新幻觉的确认
Some hypotheses on how chatbots work in problem-solution-driven conversations: Large Language Models as confirmation of the Innovation Illusion
AI总结：
本文提出聊天机器人作为对话伙伴的本质，基于聚合动力学、认知语言学等理论，假设LLM训练数据仅部分模仿人类思维，并得出结论：基础聊天机器人无法成为与人类匹敌的思考伙伴。
摘要：
本文提供了一种关于聊天机器人在讨论问题及其解决方案时作为真正对话伙伴的本质的视角。聊天机器人能做什么，不能做什么，以及如何解释这一点？我们的论证借鉴了聚合动力学、认知语言学、神经心理学和心理学。我们的论证聚焦于基础聊天机器人，希望借此对更高级聊天机器人的核心功能做出陈述。基础聊天机器人被假定为由一个带有简单界面的大型语言模型（LLM）组成。主要结果是：基于所谓隐喻问题传播的人类理解和思维描述；用于训练LLM的文本数据集具有特定特征，且这些文本数据集仅部分模仿人类思维和理解的假设；LLM训练过程从这些数据集中将人工隐喻问题传播编码到LLM中的假设；我们的结论是基础聊天机器人不能成为能够与人类匹敌的思考伙伴；我们的结论是大型语言模型的进一步发展也不会导致这一点。Yann LeCun 指出：“动物和人类表现出的学习能力和对世界的理解远超当前AI和机器学习系统的能力。”我们的结论与此一致。LeCun的愿景和我们的愿景与大型科技公司的乐观主义相悖。但这并不改变聊天机器人存在的事实，它们被个人和组织大规模使用，因此从社会和政治角度理解它们很重要。我们的文章旨在为关于聊天机器人功能、优点和缺点的讨论做出贡献。在我们对聊天机器人工作原理的研究中，我们尚未遇到用于得出我们结论的方法。
英文摘要：
We discuss the nature of chatbots as conversation partners in problem-solving conversations. What can chatbots do and what can't they do? Our analysis draws on insights from Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology. We establish that chatbots are multifaceted and composite systems. Our argument focuses on basic chatbots in the hope of thereby making statements about the core functionality of more advanced chatbots. Basic chatbots are assumed to consist of a Large Language Model (LLM) with a simple interface. The main results of our analysis are: a description of human imagination, understanding and thinking based on so-called metaphorical problem propagations; that the texts in text datasets used for training LLMs have specific characteristics and that these texts only partially imitate human thinking and understanding; that the LLM training process encodes artificial metaphorical problem propagations into an LLM from these text datasets. Our conclusions are that a basic chatbot cannot be a thinking partner capable of matching the cognitive flexibility of humans, and that further development of LLMs will not lead to this either. But chatbots exist, they are being used on a massive scale, by both individuals and organisations. It is therefore socially and politically important to understand them. Our article aims to contribute to the discussion on the functioning, benefits and drawbacks of chatbots. Cognitive Linguistics shows how the use of metaphor is an expression of our thinking. Aggregation Dynamics, is an attempt at a comprehensive systems theory. We believe that the concept of metaphorical problem propagation could provide an interesting addition for both. Chatbots a solution? For what?
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。
