The Problem of Alignment
Tsvetelina Hristova Liam Magee Karen Soldatic
Large Language Models produce sequences learned as statistical patterns from
large corpora. In order not to reproduce corpus biases, after initial training
models must be aligned with human values, preferencing certain continuations
over others. Alignment, which can be viewed as the superimposition of normative
structure onto a statistical model, reveals a conflicted and complex
interrelationship between language and technology. This relationship shapes
theories of language, linguistic practice and subjectivity, which are
especially relevant to the current sophistication in artificially produced
text. We examine this practice of structuration as a two-way interaction
between users and models by analysing how ChatGPT4 redacts perceived
`anomalous' language in fragments of Joyce's Ulysses and the new linguistic
practice of prompt engineering. We then situate this alignment problem
historically, revisiting earlier postwar linguistic debates which counterposed
two views of meaning: as discrete structures, and as continuous probability
distributions. We discuss the largely occluded work of the Moscow Linguistic
School, which sought to reconcile this opposition. Our attention to the Moscow
School and later related arguments by Searle and Kristeva casts the problem of
alignment in a new light: as one involving attention to the social
structuration of linguistic practice, including structuration of anomalies
that, like the Joycean text, exist in defiance of expressive conventions. These
debates around the communicative orientation toward language can help explain
some of the contemporary behaviours and interdependencies that take place
between users and LLMs.