Title: How Large Language Models (LLMs) Extrapolate: From Guided Missiles to Guided Prompts Show Chinese title

Title: 如何让大型语言模型（LLMs）进行外推：从制导导弹到制导提示

Abstract: This paper argues that we should perceive LLMs as machines of extrapolation. Extrapolation is a statistical function for predicting the next value in a series. Extrapolation contributes to both GPT successes and controversies surrounding its hallucination. The term hallucination implies a malfunction, yet this paper contends that it in fact indicates the chatbot efficiency in extrapolation, albeit an excess of it. This article bears a historical dimension: it traces extrapolation to the nascent years of cybernetics. In 1941, when Norbert Wiener transitioned from missile science to communication engineering, the pivotal concept he adopted was none other than extrapolation. Soviet mathematician Andrey Kolmogorov, renowned for his compression logic that inspired OpenAI, had developed in 1939 another extrapolation project that Wiener later found rather like his own. This paper uncovers the connections between hot war science, Cold War cybernetics, and the contemporary debates on LLM performances. Show Chinese abstract

Abstract: 本文认为，我们应该将大型语言模型视为外推机器。 外推是一种统计函数，用于预测序列中的下一个值。 外推既有助于GPT的成功，也引发了关于其幻觉的争议。 “幻觉”一词暗示了故障，但本文认为，实际上它表明了聊天机器人的外推效率，尽管是过度的外推。 本文具有历史维度：它追溯了外推到控制论的萌芽时期。 1941年，当诺伯特·维纳从导弹科学转向通信工程时，他采用的关键概念就是外推。 苏联数学家安德烈·柯尔莫哥洛夫，因其启发OpenAI的压缩逻辑而闻名，他在1939年开发了一个其他的外推项目，后来维纳发现这与他自己的非常相似。 本文揭示了热战时期的科学、冷战时期的控制论以及当代关于大型语言模型表现的争论之间的联系。