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Implicit Reasoning in Transformers: Bridging the Compositionality Gap in GPT-2-like Models
Compositional generalization remains a persistent limitation of transformer language models: even when a model learns to combine known facts correctly in-distribution (ID), the same compositional mechanism may fail when applied to out-of-distribution (OOD) knowledge. We investigate this compositiona
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