The asymmetric harms of LLM compression reduce head knowledge retention more than overall knowledge across 3 models and 11 compression methods.
Read the original at arxiv.org→arXiv:2608.19670v1 Announce Type: new Abstract: Large language models (LLMs) compression reduces deployment costs, but standard aggregate metrics like perplexity and accuracy often mask underlying behavioral shifts....
Original headline: "The Asymmetric Harms of LLM Compression"
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- Aug 21, 04:00 UTC arXiv cs.CL lead source The Asymmetric Harms of LLM Compression