核心概念
The author explores the capabilities and limitations of Large Language Models (LLMs) through Heidegger's philosophical concepts, shedding light on their potential to emulate human reasoning.
摘要
The content delves into the comparison between LLMs and human reasoning systems, highlighting the structural analysis of reasoning processes. It discusses how LLMs can emulate human reasoning through "ready-to-hand" and "present-at-hand" links, categorizing reasoning into non-creative and creative types. The exploration is guided by Heidegger's concept of truth as "unconcealment," providing a unique perspective on AI capabilities.
The article also touches upon Kant's contributions to understanding reason and knowledge, emphasizing the importance of uncovering hidden truths in the reasoning process. By structuring reasoning into different categories based on its functions, the author aims to provide a comprehensive framework for evaluating LLMs' abilities in relation to human cognition.
統計資料
GPT-4 excelled in bar, SAT, and LSAT exams (Zhong et al. 2023); (Katz et al. 2023)
Google’s PaLM 2(Anil et al. 2023) advanced with compute-optimal scaling, enriched dataset mixtures.
Instruct GPT(Ouyang et al. 2022), Google's LaMDA (Thoppilan et al. 2022), Megatron-Turing NLG (Smith et al. 2022) extended LLM capabilities uniquely.
引述
"To say that an assertion 'is true' signifies that it uncovers the entity as it is in itself." - Heidegger
"The quest for truth is an active process of shedding light on aspects of reality that were previously obscured." - Author
"Reasoning seeks to uncover hidden truths, beginning with an attempt to reveal the concealed." - Author