The sociolinguistic foundations of language modeling.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2025-01-13 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1472411
Jack Grieve, Sara Bartl, Matteo Fuoli, Jason Grafmiller, Weihang Huang, Alejandro Jawerbaum, Akira Murakami, Marcus Perlman, Dana Roemling, Bodo Winter
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引用次数: 0

Abstract

In this article, we introduce a sociolinguistic perspective on language modeling. We claim that language models in general are inherently modeling varieties of language, and we consider how this insight can inform the development and deployment of language models. We begin by presenting a technical definition of the concept of a variety of language as developed in sociolinguistics. We then discuss how this perspective could help us better understand five basic challenges in language modeling: social bias, domain adaptation, alignment, language change, and scale. We argue that to maximize the performance and societal value of language models it is important to carefully compile training corpora that accurately represent the specific varieties of language being modeled, drawing on theories, methods, and descriptions from the field of sociolinguistics.

在本文中,我们将从社会语言学的角度介绍语言建模。我们认为,一般来说,语言模型本质上是对语言种类的建模,并探讨了这一观点如何为语言模型的开发和部署提供依据。我们首先介绍了社会语言学对语言种类概念的技术定义。然后,我们讨论了这一观点如何帮助我们更好地理解语言建模中的五个基本挑战:社会偏见、领域适应、对齐、语言变化和规模。我们认为,要最大限度地提高语言模型的性能和社会价值,就必须借鉴社会语言学领域的理论、方法和描述,精心编制能准确代表所建模的特定语言种类的训练语料库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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