Analogical Structure, Minimal Contextual Cues and Contrastive Distractors: Input Design for Sample-Efficient Linguistic Rule Induction (2026.eacl-long)
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| Challenge: | Recent systems that use analogical reasoning require extensive knowledge engineering and even transformer-based models show inconsistent results across complexity levels. |
| Approach: | They propose to implement analogical structure, contrastive learning, and minimal contextual cue principles into large language models that train on English verb alternations. |
| Outcome: | The proposed models learn the alternation rules with high F1 on English verb alternations. |
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