Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915939121889280 |
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| author | Yamaguchi, Atsuki Mi, Maggie Aletras, Nikolaos |
| author_facet | Yamaguchi, Atsuki Mi, Maggie Aletras, Nikolaos |
| contents | 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 its acquisition, while maintaining competitive performance on general reasoning tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_03448 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks Yamaguchi, Atsuki Mi, Maggie Aletras, Nikolaos Computation and Language 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 its acquisition, while maintaining competitive performance on general reasoning tasks. |
| title | Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.03448 |