Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

• URL: http://arxiv.org/abs/2601.03448v1
• Date: Tue, 06 Jan 2026 22:28:15 GMT
• Title: Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
• Authors: Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras,
• Abstract summary: We propose L2T, a pre-training framework integrating Language Learning Tasks alongside standard next-token prediction.<n>Inspired by human language acquisition, L2T transforms raw text into structured input-output pairs to provide explicit linguistic stimulation.
• Score: 31.93996004711539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 its acquisition, while maintaining competitive performance on general reasoning tasks.

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