CLASS-IT: Conversational and Lecture-Aligned Small-Scale Instruction
  Tuning for BabyLMs
Authors: name: Luca Capone, name: Alessandro Bondielli, name: Alessandro Lenci
Organization: CoLingLab | Computational Linguistics Laboratory - University of Pisa

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.