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This study reveals how large language models internally encode phonological rules to select morphemes like "a/an," demonstrating that they utilize linear directions in token embeddings for causal, rule-like generalization rather than simple memorization. The research provides a mechanistic account of this capability and distinguishes it from explicit metalinguistic judgment, offering critical insight into the internal representations governing natural language generation.
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How Do Language Models Represent and Use Phonological Information for Allomorph Selection?

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2609.04708v1 Announce Type: new Abstract: Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably produce morphemes whose form is phonologically conditioned. It remains unclear whether they rely on item-specific memorization or rule-like generalization and, if the latter, how that generalization is implemented. We therefore ask whether this phonological condition is represented within language models and how it is causally used for allomorph selection. For the English indefinite article a/an, we show that the phonological condition is encoded along a single linear direction in trigger-token embeddings, that this direction causally drives article selection in token-level wug tests, and that, at the article-prediction position, the model forecasts the upcoming trigger token and uses the forecasted trigger's phonological feature to choose the article. We then ask whether this rule-like generalization extends beyond English article selection, both to allomorph selection in other languages and to explicit phonological judgment. Together, these results provide a mechanistic account of phonologically conditioned allomorph selection in language models, and dissociate this generation-time ability from explicit metalinguistic judgments.

Title:How Do Language Models Represent and Use Phonological Information for Allomorph Selection?

Abstract:Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably produce morphemes whose form is phonologically conditioned. It remains unclear whether they rely on item-specific memorization or rule-like generalization and, if the latter, how that generalization is implemented. We therefore ask whether this phonological condition is represented within language models and how it is causally used for allomorph selection. For the English indefinite article a/an, we show that the phonological condition is encoded along a single linear direction in trigger-token embeddings, that this direction causally drives article selection in token-level wug tests, and that, at the article-prediction position, the model forecasts the upcoming trigger token and uses the forecasted trigger's phonological feature to choose the article. We then ask whether this rule-like generalization extends beyond English article selection, both to allomorph selection in other languages and to explicit phonological judgment. Together, these results provide a mechanistic account of phonologically conditioned allomorph selection in language models, and dissociate this generation-time ability from explicit metalinguistic judgments.

Computation and Language (cs.CL)

arXiv:2609.04708 [cs.CL]

(or arXiv:2609.04708v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.04708 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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