Not all linguistic variation is equally predictable

Abstract We compare to what extent the choice of a variant can be predicted from language-internal factors for two linguistic variables: the English dative alternation and the omission of the infinitival marker att in a Swedish future-tense construction. Previous research has shown that for the dative alternation, near-ceiling performance can be achieved by fitting a regression model with manually selected predictors. Similar attempts for att -omission have been unsuccessful. To test whether the two variables differ in predictability or whether optimal methods have not been found for att -omission, we apply a large language model (LLM) to the same task. For the dative alternation, LLM and regression perform equally well. For att -omission, LLM outperforms regression, but still performs worse than for the dative alternation, thus suggesting that att -omission is inherently less predictable. We argue that LLMs can be useful for estimating how much the choice of a variant depends on language-internal factors.

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Publication Details

Journal
International Journal of Corpus Linguistics
Published
2026-06-16
DOI
https://doi.org/10.1075/ijcl.24065.mor
Primary Topic
Language and cultural evolution
Type
article
Field-Weighted Citation Impact
0.00

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article

Not all linguistic variation is equally predictable

Aleksandrs Berdičevskis, Felix Morger
International Journal of Corpus Linguistics
Language and cultural evolution
article

Not all linguistic variation is equally predictable

Aleksandrs Berdičevskis, Felix Morger
article en

Abstract

Abstract We compare to what extent the choice of a variant can be predicted from language-internal factors for two linguistic variables: the English dative alternation and the omission of the infinitival marker att in a Swedish future-tense construction. Previous research has shown that for the dative alternation, near-ceiling performance can be achieved by fitting a regression model with manually selected predictors. Similar attempts for att -omission have been unsuccessful. To test whether the two variables differ in predictability or whether optimal methods have not been found for att -omission, we apply a large language model (LLM) to the same task. For the dative alternation, LLM and regression perform equally well. For att -omission, LLM outperforms regression, but still performs worse than for the dative alternation, thus suggesting that att -omission is inherently less predictable. We argue that LLMs can be useful for estimating how much the choice of a variant depends on language-internal factors.

International Journal of Corpus Linguistics
University of Gothenburg (SE)
Vetenskapsrådet, Göteborgs Universitet, Marcus och Amalia Wallenbergs minnesfond
Quality Education
Openalex Percentile: Top 2%
Language and cultural evolution
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