Five instruction-tuned LLMs show significant, dataset-dependent asymmetries in inverse relation classification, with relation descriptions rarely helping and performance shifting when entities are replaced or masked.
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Reversing Arrows in Large Language Models
Five instruction-tuned LLMs show significant, dataset-dependent asymmetries in inverse relation classification, with relation descriptions rarely helping and performance shifting when entities are replaced or masked.