Pruning attention layers in five LLMs across eight datasets maintains accuracy but degrades faithfulness and calibration.
An unsupervised approach to achieve supervised-level explainability in healthcare records
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Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration
Pruning attention layers in five LLMs across eight datasets maintains accuracy but degrades faithfulness and calibration.