The Sociolinguistic Foundations of Language Modeling
This work addresses the challenge of making language models more effective and socially responsible for developers and researchers, though it is incremental as it builds on existing sociolinguistic concepts.
The paper tackles the problem of improving large language models by applying a sociolinguistic perspective, arguing that they model language varieties and that this insight can address challenges like bias and domain adaptation, with the result being a call for more carefully curated training corpora to enhance performance and societal value.
In this paper, we introduce a sociolinguistic perspective on language modeling. We claim that large language models are inherently models of varieties of language, and we consider how this insight can inform the development and deployment of large 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 can help address five basic challenges in language modeling: social bias, domain adaptation, alignment, language change, and scale. Ultimately, we argue that it is crucial to carefully define and compile training corpora that accurately represent the specific varieties of language being modeled to maximize the performance and societal value of large language models.