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.

翻译：儿童与照料者之间的多轮对话具有一种称为偶发性的特性——即对话者之间及时、直接且有意义的交流。我们提出ContingentChat这一师生框架，用于在基于1亿词训练的BabyLM中评估并提升多轮对话的偶发性。通过使用新颖的后训练对齐数据集，BabyLM生成的回应在语法和连贯性方面均得到改善。采用自适应教师解码策略的实验显示增益有限。ContingentChat证明了针对性后训练对对话质量的提升作用，并表明偶发性仍是BabyLM面临的重要挑战。