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cs.CL2026
The Asymmetric Harms of LLM Compression
Yuan Wu, Mairui Li, Lesia Semenova +1
Large language models (LLMs) compression reduces deployment costs, but standard aggregate metrics like perplexity and accuracy often mask underlying behavioral shifts. In this work…
cs.CL2026
Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Cheng Zhang, Rui Xin, Chudi Zhong
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods…