Multi-turn dialogues between a child and a caregiver are characterized by a property called contingency - that is, prompt, direct, and meaningful exchanges between interlocutors. We introduce ContingentChat, a teacher-student framework that benchmarks and improves multi-turn contingency in a BabyLM trained on 100M words. Using a novel alignment dataset for post-training, BabyLM generates responses that are more grammatical and cohesive. Experiments with adaptive teacher decoding strategies show limited additional gains. ContingentChat demonstrates the benefits of targeted post-training for dialogue quality and indicates that contingency remains a challenging goal for BabyLMs.
@article{arxiv.2510.20411,
title = {Teacher Demonstrations in a BabyLM's Zone of Proximal Development for Contingent Multi-Turn Interaction},
author = {Suchir Salhan and Hongyi Gu and Donya Rooein and Diana Galvan-Sosa and Gabrielle Gaudeau and Andrew Caines and Zheng Yuan and Paula Buttery},
journal= {arXiv preprint arXiv:2510.20411},
year = {2025}
}
Comments
Outstanding Paper Award, EMNLP 2025 BabyLM Workshop - Oral presentation, Suzhou, China