描写还是解释:由ChatGPT反思语言学的两种目标
How versus Why:Reflections on the Two Objectives of Linguistics by Means of ChatGPT
袁毓林1
作者信息
- 1. 澳门大学 人文学院中国语言文学系 澳门 999078;北京大学 中文系/中国语言学研究中心 北京 100871
- 折叠
摘要
本文在现代大语言模型语境下反思语言学研究的两种目标之争:精确描写(语言事实,how)还是科学解释(语言能力,why)?以此为中心,讨论了一系列相关的问题,并考察了ChatGPT能否捕获长距离依存、能否理解句法与语义分离的句子、对语言的科学解释与精确描写是否对立.得出的结论是:(1)ChatGPT等大模型能够超越马尔可夫过程模型,来捕获语句中不同词语之间的长距离依存关系;能够隐式地学习基本的句法和语义知识,从而理解、识别和生成语义异常的句子.(2)对语言的精确描写和科学解释并不对立,并且前者比后者更加重要.(3)生成语法学的"原则与参数"范式下的范畴语法,对于描写人类自然语言有不可克服的困难.(4)语法学的研究取向应该是语义优先,而不是句法优先.(5)大模型的成功说明:对语言事实的准确描写远比对语言能力的抽象解释更为基本.
Abstract
Since its public release at the end of 2022,ChatGPT has attracted worldwide attention.Much research has been conducted on the opportunities and challenges ChatGPT has brought to linguistic studies.At the same time,scholars hold different views on the roles of ChatGPT in linguistic studies.This paper begins with Norvig's(2011)argument on two competing goals in linguistic studies:descriptive accuracy(of linguistic performance,that is,how)and scientific explanation(of linguistic competence,that is,why).Centered on this issue,a series of related questions are discussed,leading to the following conclusions:(1)ChatGPT and Large Language Models(LLMs)can surpass Markov Process Model to capture long-distant dependency holding between different words in a sentence.They can implicitly learn basic syntactic and semantic knowledge,enabling them to understand,recognize,and generate semantically anomalous sentences.(2)Descriptive accuracy and scientific explanation do not contradict each other,and the former is more important than the latter in linguistic studies.(3)Categorical grammar within the"principles and parameters"paradigm of generative grammar faces insurmountable difficulties in describing human natural language.(4)The approach of grammar study should prioritize semantics over syntax.(5)The success of LLMs shows that the descriptive accuracy of linguistic performance is far more basic than abstract explanation of linguistic competence.
关键词
ChatGPT/语言模型/描写/解释/语言事实/语言能力/语义优先/句法优先Key words
ChatGPT/language model/description/explanation/linguistic performance/linguistic competence/semantic priority/syntactic priority引用本文复制引用
出版年
2025
NETL
NSTL
万方数据