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

📄 arXiv: 2403.03288v1 📥 PDF

作者: Jianqiiu Zhang

分类: cs.AI

发布日期: 2024-03-05

备注: 39 pages

💡 一句话要点

通过海德格尔哲学分析人类推理系统以评估大型语言模型的能力与风险

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大型语言模型 人类推理 海德格尔哲学 推理能力 人工智能伦理

📋 核心要点

  1. 当前大型语言模型在推理能力上存在局限，尤其是在真实理性和创造性推理方面。
  2. 本文通过海德格尔的哲学框架，分析LLMs的推理能力与人类推理的结构，提出新的分类方法。
  3. 研究表明，LLMs在直接解释性推理和伪理性推理方面表现良好，但在创造性推理上仍显不足。

📝 摘要（中文）

在快速发展的大型语言模型（LLMs）领域，深入分析其能力与风险显得尤为重要。本文提出了两个创新要素：首先，探讨了LLMs中词关系的统计模式与海德格尔的“随手可得”和“在手可见”概念之间的平行关系，揭示LLMs作为数字化的语言知识能力的潜力。其次，通过海德格尔的“真理即揭示”观点对人类推理进行结构分析，将推理分为四个类别，明确LLMs在更广泛的人类推理框架中的位置。研究发现，尽管LLMs在某些推理能力上表现出色，但在真实理性推理和创造性推理方面仍存在不足，尚未达到人类智力的水平。

🔬 方法详解

问题定义：本文旨在解决大型语言模型在推理能力上的不足，特别是缺乏真实理性和创造性推理的现象。现有方法未能充分揭示LLMs与人类推理之间的关系。

核心思路：通过海德格尔的哲学视角，建立LLMs与人类推理的结构性比较，明确LLMs的能力与局限性，从而为理解其在推理中的角色提供新的视角。

技术框架：研究分为两个主要模块：一是分析LLMs中词关系的统计模式，二是基于海德格尔的真理观对人类推理进行结构分析，最终将LLMs置于人类推理的框架中。

关键创新：最重要的创新在于将海德格尔的哲学概念应用于LLMs的分析，提出了推理的四个类别，并明确了LLMs在这些类别中的位置，揭示其在推理能力上的优势与不足。

关键设计：在分析过程中，采用了海德格尔的“随手可得”和“在手可见”概念作为理论基础，结合推理的结构性分析，确保了对LLMs能力的全面评估。

📊 实验亮点

研究结果表明，尽管LLMs在直接解释性推理和伪理性推理方面表现出色，但在真实理性推理和创造性推理上仍存在显著不足。LLMs尚未达到人类智力的水平，尤其在缺乏判断能力的情况下，无法实现真正的创造性思维。

🎯 应用场景

本研究为理解大型语言模型的能力与局限性提供了新的理论框架，具有广泛的应用潜力，尤其在人工智能伦理、教育技术和人机交互等领域。未来，研究结果可为改进LLMs的设计和应用提供指导，推动人工智能技术的健康发展。

📄 摘要（原文）

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.