Thailand

We develop a multilingual version of the Wug Test, an artificial word completion experiment that is typically used to test the morphological knowledge of children, and apply it to the GPT family of large language models (LLMs). LLMs&#39; performance on this test was evaluated by native speakers of six different languages, who judged whether the inflected and derived forms generated by the models conform to the morphological rules of their language. Our results show that LLMs can generalize their morphological knowledge to new, unfamiliar words, but that their success in generating the &quot;correct&quot; generalization (as judged by native human speakers) is predicted by a language&#39;s morphological complexity (specifically, integrative complexity). We further find that the amount of training data has surprisingly little on LLMs&#39; morphological generalization abilities within the scope of the analyzed languages. These findings highlight that ``morphology matters&#39;&#39;, and have important implications for improving low-resource language modeling.

ACL 2024

Morphology Matters: Probing the Cross-linguistic Morphological Generalization Abilities of Large Language Models through a Wug Test

cross-linguistic analysis

morphological generalization

wug test

morphological complexity

low-resource languages

morphology

large language models

multilingual language models

We develop a multilingual version of the Wug Test, an artificial word completion experiment that is typically used to test the morphological knowledge of children, and apply it to the GPT family of large language models (LLMs). LLMs' performance on this test was evaluated by native speakers of six different languages, who judged whether the inflected and derived forms generated by the models conform to the morphological rules of their language. Our results show that LLMs can generalize their morphological knowledge to new, unfamiliar words, but that their success in generating the "correct" generalization (as judged by native human speakers) is predicted by a language's morphological complexity (specifically, integrative complexity). We further find that the amount of training data has surprisingly little on LLMs' morphological generalization abilities within the scope of the analyzed languages. These findings highlight that ``morphology matters'', and have important implications for improving low-resource language modeling.

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Morphology Matters: Probing the Cross-linguistic Morphological Generalization Abilities of Large Language Models through a Wug Test

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Evaluating Grammatical Well-Formedness in Large Language Models: A Comparative Study with Human Judgments

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