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        <title>Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks</title>
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        <namePart type="given">Atsuki</namePart>
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        <namePart type="family">Mi</namePart>
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    <name type="personal">
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            <title>Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
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            <namePart type="given">Maria</namePart>
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            <namePart type="given">Viviane</namePart>
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    <abstract>Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning, it does not explicitly optimize for linguistic competence. To bridge this gap, we propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next-token prediction. Inspired by human language acquisition, L2T transforms raw text into structured input-output pairs to provide explicit linguistic stimulation. Pre-training LMs on a mixture of raw text and L2T data not only improves overall performance on linguistic competence benchmarks but accelerates their acquisition, while maintaining competitive performance on general reasoning tasks.</abstract>
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%0 Conference Proceedings
%T Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
%A Yamaguchi, Atsuki
%A Mi, Maggie
%A Aletras, Nikolaos
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-391-3
%F yamaguchi-etal-2026-enhancing
%X Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning of world knowledge and reasoning, it does not explicitly optimize for linguistic competence. To bridge this gap, we propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next-token prediction. Inspired by human language acquisition, L2T transforms raw text into structured input-output pairs to provide explicit linguistic stimulation. Pre-training LMs on a mixture of raw text and L2T data not only improves overall performance on linguistic competence benchmarks but accelerates their acquisition, while maintaining competitive performance on general reasoning tasks.
%U https://aclanthology.org/2026.acl-short.27/
%P 316-336

Markdown (Informal)

[Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://aclanthology.org/2026.acl-short.27/) (Yamaguchi et al., ACL 2026)

• Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks (Yamaguchi et al., ACL 2026)

ACL

• Atsuki Yamaguchi, Maggie Mi, and Nikolaos Aletras. 2026. Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 316–336, San Diego, California, United States. Association for Computational Linguistics.