At 60% SparseGPT sparsity, mixing four capability-matched calibration corpora preserves 58.8% of unpruned performance versus 40.0% for C4 — but the mix is partially calibrated on the same GSM8K and MBPP benchmarks used for evaluation.
arXiv preprint arXiv:2310.00867 , year=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Averaged Evaluation Masks Capability Trade-Offs: Multi-Source Calibration for High-Sparsity LLM Pruning
At 60% SparseGPT sparsity, mixing four capability-matched calibration corpora preserves 58.8% of unpruned performance versus 40.0% for C4 — but the mix is partially calibrated on the same GSM8K and MBPP benchmarks used for evaluation.