解析大模型如何理解语言，对比人类与机器的语义学习差异。
A Primer on Computational Semantics for Artificial Intelligence Systems
- 梳理形式、具身、分布三类语义理论框架
- 揭示大模型语义表征与人类认知的本质区别
- 适合对AI语言理解机制感兴趣的从业者
随着基于Transformer的语言模型（如ChatGPT和Gemini）在越来越多场景中应用，理解这些模型如何学习和表示语言意义，以及对语言本质有更深入认识变得尤为重要。本文旨在帮助读者了解语言意义（即语义）在科学与哲学不同领域中的研究路径。文章阐述了三种主要语义理论：形式语义学、具身语义学和分布语义学，并比较了基于Transformer的语言模型与人类习得语言方式的差异。
原文摘要 · Abstract (English)
As people adopt transformer-based language models (e.g., ChatGPT and Gemini) for an increasing number of use-cases, it is important to know how such models learn and represent the meaning of the language, and to be more informed about what language is. This document is an attempt to help the reader understand how linguistic meaning (i.e., semantics) is approached from different fields of scientific and philosophical examination. I also explain three primary semantic theories: formal semantics, grounded semantics, and distributional semantics then compare how transformer-based language models differ from how humans learn language.
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