arXiv Machine Learning June 25, 2026 By Pietro Tropeano, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration

Read the original on arXiv Machine Learning →

arXiv:2606. 24970v1 Announce Type: new Abstract: Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the network, producing smaller models that retain most of their accuracy.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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