Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion

Title: Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion

标题：关于聊天机器人在解决问题导向对话中如何运作的若干假设：大语言模型作为“创新幻觉”的佐证

Abstract: This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions. What can chatbots do and what can’t they do, and how can this be explained? Our argument draws on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology.

摘要： 本文从聊天机器人在探讨问题及其解决方案时，是否能作为真正的对话伙伴这一视角展开论述。聊天机器人能做什么，不能做什么？这又该如何解释？我们的论点借鉴了聚合动力学（Aggregation Dynamics）、认知语言学、神经心理学和心理学。

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.

我们的论点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判断。我们假设基础聊天机器人由一个大语言模型（LLM）和一个简单的交互界面组成。

The main results are: a description of human understanding and thinking based on so-called metaphorical problem propagations; the hypothesis that text dataset used for training LLMs have specific characteristics and that these text datasets only partially imitate human thinking and understanding; the hypothesis that the LLM training process encodes artificial metaphorical problem propagations into an LLM from these datasets; our conclusion that a basic chatbot cannot be a thinking partner capable of matching humans; our conclusion that further development of the Large Language Model will not lead to this either.

主要研究结果包括：基于所谓的“隐喻性问题传播”对人类理解与思维的描述；关于用于训练大语言模型的文本数据集具有特定特征，且这些数据集仅能部分模拟人类思维与理解的假设；关于大语言模型训练过程将这些数据集中的人工隐喻性问题传播编码进模型内部的假设；我们得出的结论是，基础聊天机器人无法成为能够与人类相媲美的思维伙伴；同时我们认为，大语言模型的进一步发展也不会改变这一现状。

Yann LeCun states: “Animals and humans exhibit learning abilities and understandings of the world that are far beyond the capabilities of current AI and machine learning (ML) systems.” Our conclusions are in line with this. LeCun’s vision and ours are at odds with the optimism of Big Tech.

杨立昆（Yann LeCun）曾表示：“动物和人类所展现出的学习能力和对世界的理解，远超当前人工智能和机器学习系统的能力。”我们的结论与此一致。立昆的愿景以及我们的观点，都与大型科技公司的乐观态度相左。

That does not alter the fact that chatbots exist, that they are being used on a massive scale, by both individuals and organisations, and that 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. We have not yet encountered the approach we used to arrive at our conclusions in our research into how chatbots work.

但这并不能改变聊天机器人已经存在、正被个人和组织大规模使用的事实，因此，从社会和政治层面去理解它们显得尤为重要。本文旨在为关于聊天机器人的运作机制、优缺点等讨论做出贡献。在我们对聊天机器人运作机制的研究中，尚未发现其他研究采用过我们得出结论时所使用的方法。