In the rapidly evolving field of Large Language Models (LLMs), there is a critical need to thoroughly analyze their capabilities and risks. Central to our investigation are two novel elements. Firstly, it is the innovative parallels between the statistical patterns of word relationships within LLMs and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand," which encapsulate the utilitarian and scientific altitudes humans employ in interacting with the world. This comparison lays the groundwork for positioning LLMs as the digital counterpart to the Faculty of Verbal Knowledge, shedding light on their capacity to emulate certain facets of human reasoning. Secondly, a structural analysis of human reasoning, viewed through Heidegger's notion of truth as "unconcealment" is conducted This foundational principle enables us to map out the inputs and outputs of the reasoning system and divide reasoning into four distinct categories. Respective cognitive faculties are delineated, allowing us to place LLMs within the broader schema of human reasoning, thus clarifying their strengths and inherent limitations. Our findings reveal that while LLMs possess the capability for Direct Explicative Reasoning and Pseudo Rational Reasoning, they fall short in authentic rational reasoning and have no creative reasoning capabilities, due to the current lack of many analogous AI models such as the Faculty of Judgement. The potential and risks of LLMs when they are augmented with other AI technologies are also evaluated. The results indicate that although LLMs have achieved proficiency in some reasoning abilities, the aspiration to match or exceed human intellectual capabilities is yet unattained. This research not only enriches our comprehension of LLMs but also propels forward the discourse on AI's potential and its bounds, paving the way for future explorations into AI's evolving landscape.

该研究通过分析和比较大型语言模型（LLMs）与马丁·海德格尔“实用存在”和“现成存在”概念之间的相似之处，以及通过基于海德格尔对真理的概念的结构性分析，探讨了LLMs在人类推理过程中的位置和局限性，发现尽管LLMs在某些推理能力上表现出色，但其与人类智力能力的匹敌还未实现。此研究丰富了我们对LLMs的理解，并推动了人工智能潜力与限制的讨论，为未来对人工智能发展趋势的探索铺平了道路。

Should We Fear Large Language Models? A Structural Analysis of the Human
  Reasoning System for Elucidating LLM Capabilities and Risks Through the Lens
  of Heidegger's Philosophy

我们应该担心大型语言模型吗？ 通过海德格尔哲学的视角对人类推理系统进行结构分析以阐明LLM的能力和风险

我们应该担心大型语言模型吗？通过海德格尔哲学的视角对人类推理系统进行结构分析以阐明LLM的能力和风险