# The Sociolinguistic Foundations
of Language Modeling

Jack Grieve
<EMAIL_ADDRESS>Sara Bartl
<EMAIL_ADDRESS>Matteo Fuoli
<EMAIL_ADDRESS>Jason Grafmiller
<EMAIL_ADDRESS>Weihang Huang
<EMAIL_ADDRESS>Alejandro Jawerbaum
<EMAIL_ADDRESS>Akira Murakami
<EMAIL_ADDRESS>Marcus Perlman
<EMAIL_ADDRESS>Dana Roemling
<EMAIL_ADDRESS>Bodo Winter
<EMAIL_ADDRESS>
Department of Linguistics and Communication
University of Birmingham

###### Abstract

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.

Keywords Large Language Models $\cdot$ Artificial Intelligence $\cdot$ Natural
Language Processing $\cdot$ Computational Sociolinguistic $\cdot$ AI Ethics

##  1 Introduction

The underlying task of language modeling is to predict the probability of a
word, or other linguistic forms, in a text based on previously observed texts
[1]. Language modeling is not new [2], but when pursued through the analysis
of extremely large corpora of natural language using transformer-based
architectures [3, 4], it has proven to be a uniquely effective approach to
natural language processing (NLP) [5]. These systems, which have come to be
known as Large Language Models (LLMs), are currently revolutionizing
Artificial Intelligence (AI), with especially powerful LLMs like GPT-4 [6] and
LLaMa [7] often being referred to as base models or foundation models [8] due
to their high levels of fluency and their ability to help achieve state-of-
the-art performance across a wide range of downstream tasks, most famously in
chatbots like ChatGPT [9]. Despite increasing concerns about the risks of LLMs
[10], experts across many fields believe they will have a major impact on
society, including in medicine [11], education [12], computer programing [13],
journalism [14], and technical writing [15].

Given the growing societal importance of LLMs, language modeling has provoked
critical discussion from a wide range of perspectives, not only AI and NLP
(e.g., [10, 8]), but in linguistics (e.g., [16, 17, 18]), cognitive science
(e.g., [19, 20, 21], and ethics (e.g., [22, 23, 24, 25, 26]). There is,
however, a very basic question about language modeling that has received
remarkably little attention in the literature:

> What is actually being modeled by language models?

Although the goal of language modeling is clear (i.e. token prediction), the
type of language being modeled by language models is only ever defined in the
most general terms, for example, “a broad swath of internet data” [27]. Models
are often trained on corpora based at least in part on the CommonCrawl dataset
[5, 28, 29], but otherwise, in most cases, the nature of the language being
modeled is not described at all [10]. In large part, this is a natural
consequence of the need for massive amounts of data to train base models,
making the sources of these corpora of secondary concern. However, even when
these models are adapted for more specific contexts [30], the type of language
used for further training is generally only loosely defined. For example,
ChatGPT was developed by adapting a GPT-3.5 base model for dialogue [31], but
the form of dialogue actually being modeled by ChatGPT is something much less
diverse and much more artificial than everyday English conversation, as anyone
who interacts with ChatGPT knows.

Drawing on modern sociolinguistic theory, in this paper, we therefore provide
an answer to the question what is being modeled by language models?

> Language models are models of varieties of language.

We argue that any language model is inherently modeling the variety of
language represented by the corpus on which it is trained, even if that
variety of language is unknown and even if that corpus is a poor
representation of that variety of language. Our view is that this simple
insight can inform, at a fundamental level, how language models are developed
and deployed in the real world. Given rapid advances in language modeling in
recent years and the increasing societal impact and risk associated with LLMs,
we believe the sociolinguistic perspective we are advocating for in this paper
is especially important at this time – not only to improve the performance,
evaluation, and applicability of LLMs, but to guide the creation of safe and
ethical AI systems and to help us better understand their underlying nature.

In the rest of this paper, we expand on our basic claim that language models
represent varieties of language and consider the implications of this claim
for the future of language modeling. We first provide a technical definition
of the sociolinguistic concept of a variety of language and argue that this
concept inherently underpins the task of language modeling. We then introduce
and discuss five general challenges in language modeling that we believe the
sociolinguistic perspective introduced in this paper can help address. We
refer to these challenges as social bias, domain adaptation, alignment,
language change, and scale. Our core message is that to maximize the value of
LLMs in society, it is crucial to carefully consider the specific varieties of
language being modeled and to compile corpora that accurately represent these
varieties of language, grounded in theories and methods developed in
sociolinguistics for understanding language variation and change.

##  2 Defining Varieties of Language

A variety of language, or more simply a variety, is a term commonly used
across linguistics to refer to any type of language [32, 33, 34, 35, 36, 37].
The term is especially common in fields that study language variation and
change – like sociolinguistics, dialectology, typology, historical
linguistics, discourse analysis, stylistics, and corpus linguistics – where it
is generally used to identify the types of language targeted for description,
comparison, or other forms of linguistic analysis.

One reason a variety of language is such a powerful concept is because it can
be used to identify a wide range of phenomena – from very broadly defined
varieties like the entire English language to very narrowly defined varieties
like the speeches of a single politician. This terminology also allows
linguists to sidestep debates, which are often underlyingly political in
nature, like whether a given variety qualifies as a dialect or a language
[38]. For example, regardless of whether Scots is considered to be a dialect
of English or a distinct language, Scots can be considered to be a variety, as
well as a sub-variety of some larger Anglic variety that also includes English
[39]. Similarly, regardless of whether Chinese is considered to be a family
composed of many languages or a language composed of many dialects, all forms
of Chinese can be considered to be both varieties themselves and part of some
larger Sinitic variety [40].

Although what are traditionally considered entire languages like English or
Chinese can be referred to as varieties, the term is most commonly used in
linguistics to refer to more narrowly defined types of language [37, 38, 41].
Such varieties are referred to by a wide range of technical and colloquial
terms, including not only dialects, but accents, sociolects, topolects,
argots, jargons, registers, genres, styles, slangs, standards, periods, and
eras. We believe, however, that it is especially insightful to recognise three
basic and distinct types of varieties – or, alternatively, three basic and
distinct sources of linguistic variation – which we refer to as dialect,
register, and period (see Figure 1).

Dialects are varieties defined by the social backgrounds and identities of the
people who produce language [42]. For example, dialects are often associated
with language that originates from speakers from a particular nation, region,
class, or ethnicity. Crucially, dialects are defined by the social
characteristics of language users. Alternatively, registers are varieties
defined by the social contexts in which people, potentially from any social
background, produce language [43]. For example, registers are often associated
with language produced in specific modalities, media, settings, and topics.
Whereas dialects are defined by the characteristics of language users,
registers are defined by the characteristics of the contexts in which those
language users communicate. Finally, periods are varieties defined by the time
span over which language is produced [44]. Taken together, these three extra-
linguistic sources of linguistic variation allow for varieties of language to
be defined with great flexibility.

The relationships between varieties can be highly complex (see Figure 1).
Varieties can be defined at any scale and are generally hierarchically
structured, being divisible into smaller and smaller sub-varieties. For
example, English is a variety, but it also contains many smaller sub-
varieties. These include many dialects, including national varieties of
English, like British and American English, which are themselves composed of
many smaller regional dialects [42]. At the most narrowly defined level, the
language of an individual can even be considered a distinct dialect (i.e., an
idiolect). Similarly, English also includes many registers, including spoken
and written English, which are themselves composed of many smaller registers,
like conversations, telephone conversations, and personal telephone
conversations [43].

Figure 1: Varieties of Language. This figure defines the concept of a variety
of language, illustrating how the interaction between three distinct extra-
linguistic factors – the social background of people who produce language
(dialect), the social context in which language is produced (register), and
the range of time over which language is produce (period) – can be used to
specify a variety of language. It also illustrates how varieties of language
are hierarchically organized, composed of smaller and smaller sub-varieties.

Along with exhibiting hierarchical structure, varieties can also be defined
based on the overlap of larger varieties (see Figure 1). For example, it is
common to define a variety of interest by specifying a dialect, register, and
period, like Contemporary Conversational Canadian French or Scottish Novels
from the Twentieth Century Written by Women. In other words, we can think of a
variety as being defined by the specification of one or more extra-linguistic
factors related to the circumstances in which language is produced. In
addition, the boundaries between varieties are not necessarily sharp or fixed.
For example, one regional dialect or literary register might transition
gradually into the next, and where we draw a line between them may change over
time.

Although we have defined a variety of language as a type of language, it is
important to specify what exactly a variety of language consists of. In other
words, when linguists study a variety of language, what are they actually
studying? For many linguists, a variety of language is essentially a
population of texts (or utterances), as circumscribed by one or more extra-
linguistic factors, in particular, by a specific dialect, register, and period
(for related discussion, see [45]). Notably, in this case, a text is broadly
defined as the language (e.g., utterances, discourse) produced during any
communicative event, including crucially language produced in any modality
(e.g., speech, writing, signing) [46]. For example, not only can an email or
an essay be considered a text, but so can a conversation or a speech. If we
adopt what is known as an externalist approach to linguistics [47, 48], where
language in general is defined as the population of all texts (or utterances)
that have ever been produced, a variety of language can then be defined as a
sub-population of those texts that meets some external definition – i.e., the
totality of language produced by people from a particular social background
(dialect), in a particular social context (register), and over a particular
period of time (period).

For example, Contemporary Spoken French Canadian Conversation can be
considered a variety of language, as it is a population of texts (i.e.,
conversations) produced by individuals from a specific social background
(i.e., people who live in Canada), in a specific social context (i.e., spoken
interactions), during a specific period (i.e., now). Similarly, a more
narrowly defined type of language like Scottish Novels from the Twentieth
Century Written by Women can also be considered a variety of language, as it
is a population of texts (i.e., books) produced by individuals from a specific
social background (i.e., female authors from Scotland), in a specific social
context (i.e., long-form fictional narratives), during a specific time span
(i.e., 1900-1999).

Notably, this conception of a variety of language is especially common in
corpus linguistics, where a corpus is often seen as representing a variety of
language: a corpus consists of a sample of texts drawn from the larger
population of texts targeted for analysis [49, 50, 35, 47]. The goal of
analyzing the structure of language observed in a corpus is therefore to draw
generalizations about the variety of language (i.e., the larger population of
texts) represented by that corpus. Furthermore, the quality of a corpus, and
by extension the generalizability of any analyses based on that corpus,
depends directly on the representativeness of this sample, including the
accurate identification of its primary constituent sub-varieties (see Figure
2).

Finally, if a variety of language is defined as a population of texts
delimited by some set of external criteria, the general expectation is that
this population of texts will differ from other populations of texts in terms
of its linguistic structure, including its grammar, phonology, lexis, and
discourse [32, 36]. For example, among other features, a regional dialect may
be characterized by the specific pronunciation of certain vowels, whereas a
conversational register might be characterized by its rate of use of certain
pronouns. Crucially, we can expect that any social group or any social context
that is recognized within society will generally become associated with
distinct patterns of linguistic variation over time, if only because, at the
most basic level, certain words associated with concepts of particular
importance to that group or context will be favored or will develop over time,
although differences can generally be expected to emerge across all levels of
linguistic analysis, depending on the communicative constraints and
affordances associated with the extra-linguistic factors that define that
variety (for discussion, see [51]). Although the number of possible varieties
is therefore innumerable, a general goal of linguistic analysis is to identify
varieties that are maximally distinctive, for example, mapping the dialect
regions of a country [52, 53], defining the sub-types of a given register [54,
55], or identifying the most distinct periods of a language [56, 57].

To summarize the discussion presented in this section, we offer the following
definition of a variety of language (see Figure 1):

> A variety of language is a population of texts defined by one or more
> external factors, especially related to the social background of the people
> who produce these texts, the social context in which these texts are
> produced, and the period of time over which these texts are produced.

Furthermore, we define a corpus as a sample of texts drawn from a specific
variety of language, i.e., from a larger population of texts (see Figure 2).
In this sense, we say that a corpus represents a given variety of language. It
is also important to stress, especially in the context of language modeling,
that any corpus – any sample of texts – inherently represents some variety of
language, namely, the smallest common variety that encompasses that sample of
texts. However, the representativeness of any corpus depends directly on the
quality and the size of the sample, as well as the accurate identification of
the variety and its sub-varieties from which texts are sampled. For example, a
sample consisting of a few conversational transcripts and emails collected in
Great Britain could be taken as representing British English, just not very
well.

Our primary contention in this paper is that language models, which are
trained on large corpora of natural language, are therefore inherently
modeling varieties of language. In other words, we conceive of language models
as models of language use – models of how language is used to create texts in
the variety of language that the corpus used to train the model represents.
Furthermore, like all linguistic models that are based on corpora of natural
language, we believe that the validity and value of a language model depends
on the degree to which the training corpus accurately represents the variety
that is effectively being modeled, which we refer to as the target variety –
even if that variety of language is unknown or underspecified. Consequently,
our claim is that understanding how to define and represent varieties of
language is of direct relevance to language modeling: we believe that many
problems that arise in language modeling result from a mismatch between the
variety of language that language models are effectively intended to represent
and the variety of language that is actually represented by the training
corpora. We believe that this perspective is not only novel but fundamental to
understanding the nature of language modeling and how to maximize the societal
value of LLMs. To support and exemplify this claim, in the remainder of this
paper, we therefore consider specific implications of this sociolinguistic
conception of language modeling for a range of different challenges currently
being faced in language modeling.

Figure 2: Representative Corpus Design. This figure presents a corpus as a
representative sample of texts taken from a given variety of language (i.e.,
from a larger population of texts delimited by relevant extra-linguistic
factors). This figure also illustrates how compiling a corpus that accurately
represents a target variety requires access to an underlying model of that
variety of language, including its internal sub-varieties, so that the corpus
can be stratified so as to capture internal variation in that variety. Naïve
corpus compilation strategies that rely on convenience sampling will generally
lead to less representative samples.

Figure 3: Sociolinguistic Bias in Language Models. This figure illustrates how
training language models on corpora that accurately represent the target
variety of language including its internal structure, especially its
constituent dialects, can help address social bias, including both quality-of-
service harms and stereotyping. This is exemplified by comparing two
hypothetical models which model American English but are trained on corpora
that inaccurately and accurately represent regional dialect variation (based
on Grieve, 2016) in this larger variety of language.

##  3 Challenges

###  3.1 Social Bias

NLP systems generally suffer from social bias: their real-world application
leads to outcomes that unfairly disadvantage or harm specific social groups
[58, 59, 60, 61]. Social bias can be introduced at various points during the
development and deployment of NLP systems [62], but given the unsupervised
nature of language modeling, training corpora are a key source of social bias
in LLMs [10, 63]. While bias in NLP systems can harm people in various ways
[59], in this section, we primarily focus on two common harmful outcomes of
social bias. These two types of harms are most commonly discussed in terms of
quality-of-service harms and stereotyping harms (e.g., [64, 65, 60, 66]),
although many different systems have been proposed for classifying biases and
harms in NLP, which define these terms in somewhat different ways, along with
many additional and often overlapping categories [59]. Both of these types of
harms are especially relevant to LLMs, and crucially, we believe both can be
better understood and addressed in language modeling by adopting a
sociolinguistic perspective (see Figure 3).

First, social bias can be characterized by poor system performance for certain
social groups that are interacting with LLMs and applications based on
language models: token prediction will be more or less accurate depending on
the social origins of the language inputted into the system. For example,
ChatGPT might have difficulty correctly understanding prompts written by
people from certain social groups due to their use of non-standard or socially
restricted language patterns. This type of bias leads to what is known as
quality-of-service harms, where the performance of these systems varies
depending on the social background of the user [64, 60]. These types of
quality-of-service harms can often be the product of selection bias, as they
result from how training data is selected from across the society whose
language is being modeled [58]: in general, if language data from certain
social groups is under-represented in the training data for a language model,
we should expect that NLP applications based on that model will process
language structures produced by these groups less accurately and consequently
exhibit poorer performance for these groups [59, 67]. Notably, quality-of-
service harms, especially those resulting from selection bias, have been one
of the central concerns in computational sociolinguistics [68, 69, 51].
Researchers in this emerging field have stressed for the past decade that the
performance of NLP systems generally varies for people from different social
groups and have called for engagement with description and theory from
sociolinguistics to help address this basic form of social bias (e.g. [70, 71,
72, 73]).

Second, social bias can be characterized by systems that produce outputs that
directly harm or discriminate against certain social groups even when they are
not directly engaging with these systems themselves. For example, when
prompted, ChatGPT might be more likely to produce negative portrayals about
ethnicities and genders, no matter who is doing the prompting [8, 67]. Most
notably, this type of bias can lead to what is known as stereotyping harms
[64], as well as related harms like disparagement and dehumanization [60],
where negative viewpoints about specific social groups are propagated, as has
been widely discussed in regards to LLMs [10]. Once again this issue can be
traced back to the data the language model was trained on. If the training
corpus contains relatively frequent expression of harmful or inaccurate ideas
about certain social groups – as we can safely assume any large, unconstrained
sample of internet writings will – language models will inevitably reproduce
those biases [10, 63]. As Bender et al. (2021, 613) state, “large, uncurated,
Internet-based datasets encode the dominant/hegemonic view, which further
harms people at the margins” [10]. These types of harms are generally the
product of semantic bias, as they result from the meaning relationships
between words inferred by the language model based on patterns of co-
occurrence observed in the training corpus [58].

From a sociolinguistic perspective, we believe that social bias in language
modeling can generally be addressed by training on corpora that more
accurately represent the target variety of language. It is especially
important that the training corpus represents the internal structure of the
target variety, in the sense that the sub-varieties of that variety of
language, including most importantly the major dialects of that variety of
language, are adequately represented in the training corpus (see Figure 3).
For example, a corpus intended to represent American English, but which is
primarily composed of texts collected from a specific dialect of American
English (e.g., texts written by highly educated, middle-class, white Americans
from major coastal cities), cannot adequately represent the full diversity of
American English. Any language model trained on such a corpus should therefore
be expected to be biased against social groups that are underrepresented in
the training data, compared to a language model trained on a corpus that more
accurately represents variation in American English.

The link between corpus design and quality-of-service harms in LLMs is
especially clear: because language varies in systematic ways, to ensure a
language model can accurately process language from a wide range of social
groups, it must be trained on corpora that represent the language used by a
wide range of social groups, i.e., their dialects (see Figure 3). For example,
consider lexical variation in British and American English: if a model were
only trained on American English, it would be much more likely to misinterpret
the meaning of words that tend to have different meanings in British English,
like boot (for trunk) or underground (for subway). Consequently, the quality
of service provided by applications based on that model for speakers of
British English would be degraded.

Stereotyping and related forms of discrimination generated by LLMs have also
often been assumed to result from careless data collection and a lack of data
curation [10]. A sociolinguistic perspective provides a principled solution to
this problem: in general, stereotyping harms can be addressed by using
training data that better represents the language produced by a wider range of
social groups. One reason that certain social groups are negatively portrayed
by LLMs is because they are not allowed to portray themselves in the data used
for training. By training on corpora that equitably and deliberately represent
the internal varietal structure of the target variety of language, especially
the range of dialects of which it is composed, stereotyping and other forms of
semantic bias can be mitigated (see Figure 3). In other words, modeling data
from a wider range of dialects helps ensure that a wider range of viewpoints
will be represented by a language model. Stratified corpora that accurately
represent the sociolinguistic structure of the target variety can also be used
to evaluate and probe a model, allowing for social bias to be identified and
interpreted directly.

The sociolinguistic approach to language modeling advocated for in this paper
therefore provides a simple yet theoretically grounded basis for understanding
the general source of social bias in language modeling, including for
addressing both quality-of-service and stereotyping harms, as well as other
related types of harms. In addition, a sociolinguistic approach offers a clear
pathway for both interpreting and addressing these different forms of social
bias during pre-training through careful corpus compilation informed by
theories and descriptions of sociolinguistic variation. Crucially, however,
such sociolinguistic interventions need not necessarily occur during the
initial pre-training of the base model, but can be pursued through the further
pre-training of base models, as we discuss in the next section.

###  3.2 Domain Adaptation

Despite their remarkable fluency and general applicability, LLMs generally
benefit from some form of domain adaptation before deployment [5, 30]. In NLP,
domain adaptation is the task of improving the performance of a system that
was developed using language data collected in one domain for a different and
often more specific domain where the system is to be applied [74]. Although
there are many approaches for adapting language models, including for
different downstream tasks (e.g., through forms of supervised and
reinforcement learning), in this case, we focus on the process of fine-tuning
a base model by extending unsupervised language modeling on a corpus of texts
sampled from a specific target domain – the real-world context where the
system is used, such as texts about a particular topic or from a particular
genre [30, 75, 76].

This approach is often referred to as further pre-training because it involves
extending the basic form of unsupervised language modeling used to train the
base model to new data from the more specific target domain [30]. The goal is
simply to improve the accuracy of token prediction in the target domain, while
preserving the underlying fluency of the base model. For example, a base model
trained on huge amounts of unrestricted online language data could be adapted
to the specific domain of customer service: based on a corpus of customer
service transcripts, the parameters of the base model would be adjusted to
improve the ability of the model to predict word tokens in texts from that
domain given the topics of discussion and the specific types of interactions
that characterize that domain.

In the context of language modeling, the process of domain adaptation can be
straightforwardly reframed directly in sociolinguistic terms (see Figure 4).
If the goal of the base model is seen as accurately predicting word tokens in
a broadly defined variety of language, like the English language, then the
goal of domain adaptation can be seen as the process of fine-tuning the base
model to allow it to predict word tokens more accurately in a more narrowly
defined variety of that language – the sub-variety associated with the target
domain. Crucially, the adapted model should be expected to be more accurate
because more narrowly defined varieties of language must be characterized by
less variation than any larger variety that encompasses it. This process can
also potentially be carried out in an iterative manner, where a base model is
repeatedly adapted on corpora representing more narrowly defined varieties of
language.

A sociolinguistic perspective on domain adaptation therefore sees the target
domain as a variety of language, which means that the process of domain
adaptation can be meaningfully informed by linguistic analysis that rigorously
identifies maximally distinctive varieties of language. This can include both
existing research in sociolinguistics, dialectology, and related fields, as
well as new research conducted directly to support model training for
specified domains. For example, if a base model is adapted for a specific
region of the US, research in American dialect geography (e.g., [53]) should
be consulted to precisely define the sub-region that is being targeted for
adaptation (see Figure 3). Similarly, if a base model is adapted for a
specific type of blog writing, research on register variation in blogs (e.g.,
[55]) should be consulted to precisely define the sub-type of blog writing
that is being targeted for adaptation.

Figure 4: Sociolinguistic Adaptation of Language Models. This figure
illustrates how an understanding of the sociolinguistic structure of varieties
of languages can inform the adaptation of language models. Language model
adaptation can be seen as the process of fine-tuning a base model, potentially
in an iterative manner, to predict word tokens in a more narrowly defined
variety of language that is subsumed by the larger variety of language
represented by the base model.

Crucially, however, sociolinguistics not only provides a basis for identifying
valid targets for domain adaptation but for mapping and modeling the internal
structure of these target varieties (see Figure 4). This is especially
important because target varieties for domain adaptation are often well-
defined by default. For example, if a fine-tuning corpus is collected by
sampling data from a particular social media platform, a relatively
homogeneous variety of language will have naturally been targeted; however, a
random sample of texts from that variety, drawn without taking into account
its internal structure, might severely under-represent sub-varieties of
interest (see Figure 2). For example, a social media corpus may be dominated
by certain sub-registers (e.g., abusive or promotional posts) that are not the
target of adaptation, while the sub-registers that are the target of
adaptation (e.g., interactive or informational posts) may be limited.
Similarly, people from certain social groups may be underrepresented in
specific domains, resulting in social bias being inadvertently exacerbated by
naive domain adaptation. In many cases, the target variety cannot even be
accurately defined until the overall structure of the larger variety in which
it is subsumed is understood through careful sociolinguistic analysis.

A sociolinguistic perspective also highlights a more general problem with
domain adaptation: the success of this process depends on the relationship
between the larger variety represented by the base model and the smaller
target variety towards which the base model is being adapted. Ideally the
variety of language represented by the base model would completely subsume the
target variety: the target variety would be a sub-variety of the base variety,
regardless of whether it was represented directly in the base training data.
However, the target variety may not be adequately represented in the data
sampled for training the base model. For example, the target variety could be
associated with a social group or a social context that is severely
underrepresented in the base training corpus. In such situations, fine-tuning
regimes informed by sociolinguistic theory and description would likely be
beneficial.

Finally, understanding the sociolinguistic structure of the larger variety of
language could also allow models to be adapted to represent target varieties
with missing data. For example, if empirical research in linguistics has found
that a target dialect or register for which data is lacking falls between
multiple dialects or registers for which data is available, a model could be
adapted for the target variety by training on a combination of the available
corpora. Overlap between varieties could also be exploited in a similar way:
for example, if data is lacking for a target variety defined in terms of a
specific register and a specific dialect, a model could be adapted for the
target variety by fine-tuning on a combination of corpora that represent that
specific dialect and that specific register. These types of techniques could
even be used to create a model of a variety of language that does not yet
exist – engineered by training on corpora representing different registers and
dialects.

###  3.3 Alignment

The related challenges of social bias and domain adaptation can be seen as
forms of the more general alignment problem – how to ensure that the behavior
of AI systems aligns with the values and expectations of society [77, 78, 79,
80, 81]. Misalignment arises not simply when AI systems fail to achieve their
intended goals, but when they pursue these goals, even successfully, in ways
that have negative or unforeseen consequences or that are not in accordance
with societal values, for example, in ways society finds to be inappropriate,
unethical, immoral, or dishonest. Alignment is therefore the general process
of guiding AI systems to behave in ways that are consistent with the broader
expectations of society, while discouraging them from behaving in ways that
are inconsistent with these expectations, especially to avoid unintended risks
and harms. Crucially, the challenge is not only how to guide AI systems but
where to guide them [77].

Although alignment is a long-standing concern in AI [82], attention has grown
in recent years due to the growing complexity and ubiquity of real-world AI
systems, especially systems based on language modeling [83, 84, 85, 86, 87],
which potentially allow for misalignment to emerge on many different levels
[77, 81]. For example, consider a generative language model that automatically
produces reviews of scientific literature on a specified topic. An obviously
misaligned system might produce reviews that are clearly wrong – incoherent or
incorrect – while a less obviously misaligned system might produce fluent
reviews, completing the task successfully in a superficial way, but getting
facts wrong, for example, referencing publications that do not exist. This
type of a hallucination – the presentation of false information as if it is
true – is a common form of misalignment in LLMs [88, 89]. A more insidiously
misaligned system, however, might produce perfectly accurate and fluent
syntheses that cite relevant literature, but exhibit other problematic
behaviors, such as limiting references to certain ideas or researchers in
certain fields, thereby effectively suppressing certain viewpoints [10].

One solution for aligning language models with the values of society is by
training these models using corpora that are in some way deemed to be more
aligned with these values (e.g., [90]. As we have argued throughout this
paper, we believe sociolinguistic theory provides a meaningful, interpretable,
and productive way to guide this process – allowing us to better understand
how corpora can be compiled so as to allow for societal expectations to be
captured, crucially without pre-specifying what these exact expectations are.

Our view is that alignment is possible if training corpora accurately
represent the range of dialects and registers of the target variety. In terms
of dialects, as we discussed when we considered the challenge of social bias,
we believe that, by balancing training data originating from different social
groups, language models can be trained to better align with the general values
of society, as opposed to the values of some particular social group.
Similarly, in terms of registers, as we discussed when we considered the
challenge of domain adaptation, we believe that, by balancing training data
originating from different communicative contexts, language models can be
trained to better align with the expectation that they will perform adequately
across the range of contexts found in that society. In other words, the values
and expectations of a society are instantiated in their patterns of language
use. In general, we therefore believe that a major source of LLM misalignment
results from what we call varietal misalignment and that LLM misalignment can
therefore be addressed, at least in part, by aligning training corpora to the
varietal structure of the target variety.

In addition to addressing alignment issues related to social bias and domain
adaptation, we believe this sociolinguistic approach can potentially help us
train models that are less susceptible to unethical and dishonest behavior in
general, because respecting sociolinguistic diversity entails training models
on data that represents a greater diversity of viewpoints, experiences, and
contexts. As LLMs are models of varieties of language, they will be better
models, more aligned with the needs, expectations, and values of society, when
they account for the full range of sub-varieties, and hence the full range of
perspectives, found within that society.

Finally, it is important to acknowledge that while a sociolinguistic
perspective provides a basis for aligning a language model to the general
viewpoints of the society that it is intended to serve, this approach does not
ensure that the resultant language model will be aligned with the ethical and
moral aspirations of that society. For example, a generative language model
trained on a socially balanced corpus of the English language will still
potentially produce texts that express racist viewpoints because a portion of
English texts expresses racist viewpoints. There might be greater equity in
the types of stereotypes it spreads, but such behavior can still be seen as a
form of misalignment. A sociolinguistic perspective, however, also provides a
possible solution to this problem – by deliberately weighting the varieties of
language represented in the training corpus. For example, if a particular
social group has been broadly disadvantaged or has a worldview that society
wishes to encourage, the portion of the corpus representing the relevant
varieties of language can be more heavily weighted during training. In this
way, a sociolinguistic perspective can provide a theoretical basis not only
for balancing but for controlling the alignment of language models.

###  3.4 Language Change

Thus far, our discussion has focused on how a series of challenges in language
modeling related to bias, adaptation, and alignment can be addressed, in
principle, by building training corpora that better represent the dialects and
registers of the target variety. Another form of this basic problem involves
ensuring that language models and applications based on language models are
responsive to language change and cultural change more generally [10, 8]. All
varieties of language change over time, often in ways that are difficult, if
not impossible, to predict [91]. If language models are to maintain their
fluency and not become obsolete, they must therefore be continuously updated
using training corpora that consist of examples of contemporary language use.
In principle, this problem can be resolved by compiling new corpora over time
that consistently represent the target variety and its evolving internal
varietal structure. The challenge is therefore to understand how the
sociolinguistic landscape of registers and dialects of that variety of
language has changed over time, which can only be accomplished accurately
through detailed and ongoing sociolinguistic analysis.

A related issue that has caused growing concern in language modeling is that
over time more and more real-world language will presumably be produced with
the assistance of LLMs, which will make it increasingly difficult to compile
contemporary corpora of real human language for training new models or
updating existing ones [92]. Proposed solutions to these problems of data
contamination [93] and task contamination [94] generally involve finding ways
to exclude machine-generated language from future training data, including
through watermarking systems [95]. These types of solutions, however, seem
easy to confound, if only because they do not generally allow texts written
collaboratively by human and machine to be identified, which is likely to
become increasingly common and diversified in everyday life.

Despite real concerns about LLM detection in certain contexts, the rising use
of LLMs to generate language is not difficult to reconcile with
sociolinguistic theory and practice. Over time, AI systems based on language
models will undoubtedly start to change how we use language. Texts generated
with the help of language models will increasingly enter into the real world.
At this point, from an externalist perspective [47], these texts will be part
of language – produced, transmitted, and understood by humans as language,
often indistinguishable from human-generated language in the regular flow of
real-world language use. Ultimately, the distinction between human- and
machine-generated language can therefore be seen as simply another aspect of
register that defines variation within varieties of language, just like all
communicative technologies that have come before, including the invention of
writing and digital communication.

Taking a sociolinguistic perspective, it is also important to acknowledge that
the rise of language models is creating new varieties of language, including
those characterized by the linguistic interaction between humans and machines,
such as dialogues with ChatGPT. These new varieties, which will only continue
to diversify over time, will also need to be accounted for, like all varieties
of language, both by theories of sociolinguistic variation and by the evolving
language models designed to represent contemporary language use. If language
models are to be kept up-to-date, machine-generated language cannot be
excluded, as its production will become a significant driver of language
change.

###  3.5 Scale

In addition to more specific insights into the development and deployment of
language models, we believe a sociolinguistic perspective can also help to
explain the remarkable success of LLMs more generally, which has been
attributed both to the development of new deep learning architectures and the
use of extremely large corpora of natural language for training [96, 10, 8].
Although there is a clear relationship between the scale of the training data
and the success of these systems, it is not altogether clear why increasing
the amount of training data results in such great increases in performance. Is
there a limit to how much performance can be gained simply by increasing the
scale of the training data? How can more powerful models be developed with
less data? These are fundamental questions for LLM development [8], especially
because of the significant costs and environmental impacts associated with
increases in scale [10]. We believe these are questions that can be uniquely
informed by a sociolinguistic perspective.

The obvious reason why increasing the amount of training data provided to the
model improves the performance of a language model is that it provides access
to a wider range of language patterns. Working with extremely large corpora is
clearly necessary – the complexity of language demands it – but it is also
clear that scale is not sufficient on its own. For example, a language model
trained repeatedly on the same dataset will not improve. What therefore
matters is not simply the scale of the training data but the diversity of the
training data.

Although the importance of the diversity of training data has often been
stressed in critiques of LLMs [27, 10], the sociolinguistic perspective
advocated in this paper provides a theoretical basis for understanding this
relationship with greater precision: diversity in the training corpus, in
terms of both its linguistic structure and its semantic content, can be seen
as directly reflecting the diversity of the varieties of language represented
by that corpus. To maximize the performance of language models and the
efficiency with which these improvements can be obtained, in our view, it is
therefore far more important to focus on increasing the varietal diversity of
the training data than purely its scale. This can be achieved by carefully
representing a wider range of contemporary language varieties in the training
corpora, including both dialects and registers, as we have discussed
throughout this paper.

This sociolinguistic perspective also provides an answer to questions about
the limits of increasing the scale of training data [8]. At what point should
increasing the size of the training corpus no longer lead to substantial
improvements in model performance? Our hypothesis is that increasing the scale
of training data will continue to increase the performance of language models
so long as it also results in an increase in the sociolinguistic diversity in
the training corpus. Crucially, this implies that attempts to empirically
assess the limits of scale simply by comparing model performance as the amount
of training data increases will not be accurate, unless the sociolinguistic
diversity of the corpus is also controlled for and measured alongside corpus
size.

Finally, a sociolinguistic perspective also offers clear direction for
training models using limited amounts of data, for example, for under-
resourced languages [10, 97]: models can be developed on a smaller scale by
taking care to maximize the amount of sociolinguistic diversity in the
training data, given the target variety. Moving forward, we therefore believe
that optimizing the development and performance of LLMs will necessarily
involve incorporating insights from sociolinguistics to enhance the diversity
and representativeness of language data used for training.

##  4 Conclusion

In this paper, we have advanced the claim that language models inherently
represent varieties of language. By extension, we have also argued that the
performance, utility, and ethical application of language models depends
directly on how well training corpora represent the varieties of language
being modeled, including their internal varietal structure. Our view is that
the societal value of language models in general depends not only on the
amount of language data used for training but on the sociolinguistic diversity
and representativeness of these corpora. We therefore believe that
incorporating insights from sociolinguistics is crucial to the future of
language modeling. To support this claim, we have identified several ways in
which a sociolinguistic perspective can provide a basis for addressing
specific challenges in language modeling related to social bias, domain
adaptation, alignment, language change, and scale in a principled and unified
manner.

Notably, there already has been considerable discussion of these types of
challenges in language modeling and NLP more generally, with proposals to
address these issues often emphasizing the need for more careful curation of
training data [10, 62] and for incorporating social and even sociolinguistic
insight into these models [98, 99, 100, 101], especially within the emerging
field of computational sociolinguistics [68, 51]. For example, to address
risks related to social bias in LLMs, Bender et al. (2021, 610) recommend that
resources must be invested for “curating and carefully documenting datasets
rather than ingesting everything on the web” [10], while Yang et al. (2024, 1)
argue that issues with LLM performance are related to “a lack of awareness of
the factors, context, and implications of the social environment in which NLP
operates, which we call social awareness” [101].

What is lacking in these discussions, however, is the proposal of a general
linguistic framework for solving these types of problems within the basic
paradigm of language modeling, especially one that is theoretically grounded
in our scientific understanding of language variation and change. Although the
lack of social diversity in training data has been repeatedly identified as a
problem for LLMs, what exactly this means and how exactly this can be measured
and addressed in a principled manner has not been articulated.

Given this emerging discourse, the primary contribution of this paper is to
propose a theoretical and empirical foundation for addressing a wide range of
challenges in language modeling that is based directly on sociolinguistic
theory, specifically the concept of a variety of language – a topic that to
the best of our knowledge has been absent from discussions of language
modeling up until now, even within computational sociolinguistics. This
perspective is also notably quite different from discussions of language
modeling in linguistics, which have focused on the status of LLMs as models of
language cognition [16, 17, 18]. In this paper, we have attempted to shift
this discussion, focusing instead on understanding language models as models
of language use, which we believe has far more direct and immediate
consequences for the development and deployment of language models in the real
world.

Our basic claim is therefore that language models can be improved in many ways
by training on datasets that endeavor to accurately represent the varieties of
language being modeled. We therefore believe that there is a clear and urgent
need for sociolinguistic insight in language model design and evaluation. At
the most basic level, language models are models of how language is used for
communication within society. Understanding the structure of society, and how
this structure is reflected in patterns of language use, is therefore critical
to maximizing the benefits of language models for the societies in which they
are increasingly being embedded. Moving forward, we believe that research on
language use – not only in sociolinguistics, but in corpus linguistics,
discourse analysis, pragmatics, cognitive linguistics, and other fields of
linguistics that focus on understanding how language is used for communication
in the real world – will increasingly become central to advancing the field of
language modeling, as well as NLP and AI more generally.

## Acknowledgement

We would especially like to thank Dong Nguyen for her comments on this paper,
as well as Meike Latz for creating the artwork presented in this paper. This
paper also benefited from discussions with Su Lin Blodgett, Dirk Hovy, Huang
He, David Jurgens, Taylor Jones, and Emily Waibel. Sara Bartl, Alejandro
Jawerbaum, and Dana Roemling were supported by the UKRI ESRC Midlands Graduate
School Doctoral Training Partnership ES/P000711/1. Bodo Winter was supported
by the UKRI Future Leaders Fellowship MR/T040505/1.

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