Large language models achieve strong performance on many tasks, but their training makes it hard to see which properties of the input support efficient linguistic rule learning. We ask how three cognitively-inspired principles of input design support sample-efficient linguistic rule induction: analogical structure, contrastive learning, and minimal contextual cue. We also ask how their effects compare to those of LLMs on the same controlled tasks. We implement these principles in structured sentence completion tasks that test English verb alternations. Lightweight models trained on hundreds to one-thousand such examples learn the alternation rules with high F1 on these tasks. Ablation studies show that analogical organisation is the main driver of sample efficiency, and contrastive distractors and minimal context help further gains. We also evaluate zero- and few-shot LLMs on the same tasks. In this controlled setting, the lightweight models reach higher F1 with far fewer task-specific data. We treat this contrast as a comparison between learning regimes rather than a general verdict on LLMs. Our results show that careful input organisation supports sample-efficient learning of linguistic rules and reveals distinct learning signatures for trained lightweight models and prompted LLMs.

翻译：大型语言模型在许多任务上表现出色，但其训练机制使我们难以识别输入的哪些特性支持高效的语言规则学习。本研究探讨了三种受认知启发的输入设计原则如何支持样本高效的语言规则归纳：类比结构、对比学习与最小上下文线索，并比较这些原则与大型语言模型在相同受控任务上的效果。我们将这些原则实现在测试英语动词交替现象的结构化句子补全任务中。仅使用数百至一千个此类示例训练的轻量级模型，在这些任务上学习交替规则时获得了较高的F1值。消融研究表明，类比组织是样本效率的主要驱动因素，而对比性干扰项与最小上下文则能带来进一步的性能提升。我们还评估了相同任务上大型语言模型的零样本与少样本表现。在此受控环境中，轻量级模型以远少于任务特定数据的需求达到了更高的F1值。我们将此差异视为不同学习机制之间的比较，而非对大型语言模型的普遍定论。研究结果表明，精细的输入组织能够支持语言规则的样本高效学习，并揭示了经过训练的轻量级模型与基于提示的大型语言模型之间不同的学习特征。