CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs

• URL: http://arxiv.org/abs/2510.25364v1
• Date: Wed, 29 Oct 2025 10:36:39 GMT
• Title: CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction Tuning for BabyLMs
• Authors: Luca Capone, Alessandro Bondielli, Alessandro Lenci,
• Abstract summary: This work investigates whether small-scale LMs can benefit from instruction tuning.<n>We compare conversational and question-answering instruction tuning datasets, applied either in a merged or sequential curriculum.<n>Results show that instruction tuning yields small but consistent gains in fine-tuning scenarios, with sequential curricula outperforming merged data.<n>However, improvements do not consistently transfer to zero-shot tasks, suggesting a trade-off between interaction-focused adaptation and broad linguistic generalization.
• Score: 81.79228604962687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work investigates whether small-scale LMs can benefit from instruction tuning. We compare conversational and question-answering instruction tuning datasets, applied either in a merged or sequential curriculum, using decoder-only models with 100M and 140M parameters. Evaluation spans both fine-tuning (SuperGLUE) and zero-shot (BLiMP, EWoK, WUGs, entity tracking, and psycholinguistic correlation) settings. Results show that instruction tuning yields small but consistent gains in fine-tuning scenarios, with sequential curricula outperforming merged data; however, improvements do not consistently transfer to zero-shot tasks, suggesting a trade-off between interaction-focused adaptation and broad linguistic generalization. These results highlight both the potential and the constraints of adapting human-inspired learning strategies to low-resource LMs, and point toward hybrid, curriculum-based approaches for enhancing generalization under ecological training limits.

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