The Problem of Alignment
Tsvetelina Hristova, Liam Magee, Karen Soldatic

TL;DR
This paper explores the complex relationship between language, technology, and human values in the alignment of large language models, analyzing historical linguistic debates and contemporary practices like prompt engineering.
Contribution
It offers a novel historical and theoretical perspective on alignment, emphasizing social structuration and anomalies in linguistic practice, informed by the Moscow Linguistic School and postwar debates.
Findings
Alignment involves social structuration of language and anomalies.
Historical linguistic debates inform current alignment challenges.
Analysis of ChatGPT4 reveals how models handle anomalous language.
Abstract
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…
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Taxonomy
TopicsLinguistic research and analysis · Language and cultural evolution · linguistics and terminology studies
