The Sociolinguistic Foundations of Language Modeling (2407.09241v1)

Abstract: In this paper, we introduce a sociolinguistic perspective on LLMing. We claim that LLMs are inherently models of varieties of language, and we consider how this insight can inform the development and deployment of LLMs. 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 LLMing: 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 LLMs.

• The paper introduces a sociolinguistic framework that refines LLM development by modeling language varieties based on social and contextual factors.
• It demonstrates that curated training corpora focusing on dialects and registers can mitigate social bias and enhance domain adaptation.
• The authors argue that effective model alignment and scaling depend on incorporating sociolinguistic diversity and monitoring language change over time.

The Sociolinguistic Foundations of LLMing

In the paper titled "The Sociolinguistic Foundations of LLMing," the authors present an insightful perspective on LLMing by integrating sociolinguistic theory. This approach acknowledges that LLMs inherently model varieties of language. The paper elaborates on how this viewpoint can refine the development and application of LLMs, addressing essential challenges in the domains of social bias, domain adaptation, alignment, language change, and the implications of data scale.

Key Concepts and Definitions

The authors begin by providing a rigorous definition of a variety of language, drawing from sociolinguistic theory. A variety of language is characterized as a population of texts determined by extra-linguistic factors such as the social background of the speakers, the communicative context, and the period over which texts are produced. This conception underscores that understanding and accurately representing these varieties are crucial to enhancing the efficacy of LLMs.

Two pivotal concepts are discussed:

1. Dialects: Varieties defined by the social backgrounds and identities of the language users.
2. Registers: Varieties identified by the social contexts in which language is used.

An illustrative hierarchical and overlapping structure of these varieties is presented, emphasizing their intricate interrelationships and the critical need for precision in their definition and representation.

Social Bias

The paper addresses social bias, a significant concern in NLP, where LLMs often exhibit uneven performance across different social groups, potentially propagating harmful stereotypes. The authors contend that social bias largely stems from unrepresentative training corpora. They advocate for the careful compilation of corpora that equitably represent the dialectal and registral diversity within the target variety. This approach enhances model performance across diverse social groups and mitigates stereotyping harms.

Domain Adaptation

Domain adaptation in LLMing involves fine-tuning pre-trained models using domain-specific corpora to improve performance in targeted contexts. The sociolinguistic view reframes this as adapting models to more narrowly defined sub-varieties of language. By leveraging sociolinguistic research to inform this process, the authors argue for more nuanced and effective adaptation, capturing the internal structure of the target variety more accurately.

Alignment

Alignment, ensuring AI systems adhere to societal values, is another focal point. Misalignment can manifest in various undesirable ways, such as producing misleading or biased outputs. The authors propose that training corpora representing the real-world varietal structure can foster alignment. This involves balancing the representation of diverse social and contextual perspectives, inherently aligning models with broader societal values and expectations.

Language Change

As languages evolve, keeping LLMs current necessitates continuous updates using recent language data. The authors highlight the need for longitudinal sociolinguistic analysis to understand and represent the changing varietal landscape accurately. They also address concerns about data contamination from machine-generated text, arguing that this text will become an integral part of language, thus needing inclusion in training datasets.

Scale

The paper posits that while increasing the scale of training data typically enhances model performance, the diversity of the training data is the crucial element. They hypothesize that model performance improvements from scaling are contingent on increased sociolinguistic diversity in the data. For under-resourced languages, maximizing sociolinguistic diversity in smaller corpora can yield more effective models, offering a pathway to efficient LLM development without extensive data requirements.

Conclusion

The authors conclude by asserting the essential role of sociolinguistics in advancing LLMing. They argue that LLMs are fundamentally models of language use, and their utility and ethical application hinge on the accurate representation of the varieties of language inherent in society. Addressing the discussed challenges—social bias, domain adaptation, alignment, language change, and scale—through a sociolinguistic lens is presented as a principled and theoretically grounded approach to improving LLMs. This paper contributes significantly to the discourse on LLM development, advocating for deeper integration of linguistic theory to enhance NLP and AI frameworks comprehensively.