揭示聊天机器人在解题对话中的认知局限，解释为何它无法真正思考。
Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion
- 用隐喻问题传播理论解析人类思维与聊天机交互机制。
- 发现训练数据仅部分模拟人类理解，模型编码了人工隐喻传播模式。
- 指出当前技术无法突破认知瓶颈，适合关注人机交互本质者阅读。
我们探讨聊天机器人作为问题解决型对话伙伴的性质：它们能做什么、不能做什么？基于聚合动力学、认知语言学、神经心理学和心理学的洞见，提出假设以解释其运作机制。研究聚焦于基础聊天机器人——即由大型语言模型（LLM）与简单界面构成的系统。核心结论包括：人类想象、理解和思维可基于‘隐喻问题传播’来描述；训练用文本数据具有特定特征，仅部分模仿人类思维；而LLM训练过程将这些人工隐喻传播编码进模型。最终认为，基础聊天机器人无法成为具备人类认知灵活性的思维伙伴，且未来改进也不会改变这一点。尽管如此，聊天机器人已被大规模使用，对个人与组织均具重要影响，因此理解其功能、优势与缺陷具有社会政治意义。本文旨在推动关于聊天机器人运作机制及其影响的讨论。认知语言学揭示隐喻是思维表达方式，聚合动力学尝试构建综合系统理论。我们认为‘隐喻问题传播’概念或可为两者提供新视角。
原文摘要 · Abstract (English)
We discuss the nature of chatbots as conversation partners in problem-solving. What can chatbots do and what can't they do? We develop hypotheses on how this can this be explained. Our argument 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 research are: a description of human imagination, understanding and thinking based on so-called metaphorical problem propagations; the hypothesis that the texts in the text dataset used for training LLMs have specific characteristics and that these texts only partially imitate human thinking and understanding; the hypothesis 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 the Large Language Model 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?
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