The paper introduces ZipCal, a model‑agnostic data curation method that selects calibration data for post‑training compression of large language models by maximizing lexical diversity using Zipfian power laws. ZipCal outperforms uniform random sampling on pruning benchmarks and matches a state‑of‑the‑art perplexity‑based approach while being roughly 240× faster due to its linear complexity. The authors provide code and experiments at their GitHub repository.
arXiv:2504.04342v2 Announce Type: replace Abstract: Scaling up model parameters and training data consistently improves the performance of large language models (LLMs), but at the cost of rapidly gro...
arXiv:2502. 07780v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant success across various NLP tasks.
arXiv:2509. 14230v2 Announce Type: replace Abstract: While structured pruning presents a highly effective pathway for accelerating Large Language Model (LLM) inference, existing methods frequently suffer from significant performance degradation and demand computationally retraining to recover capabilities.
arXiv:2606. 07819v1 Announce Type: new Abstract: Recently, the efficiency of Large Language Models (LLMs) deployment has become a critical concern in practical applications.
arXiv:2609.12475v1 Announce Type: new Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluati...
Debias‑SparseGPT is a post‑training pruning technique that adds a representational debiasing term based on demographically contrasting inputs to mitigate bias amplification caused by weight sparsification. The method is validated across various generative LLMs and sparsity levels (25%, 50%, and structured 2:4), consistently reducing pruning‑induced bias while maintaining perplexity and zero‑shot accuracy. In the most aggressive 2:4 sparsity regime, enriching the calibration set with long‑context, content‑rich examples further improves both downstream performance and fairness.
arXiv:2603. 13418v2 Announce Type: replace Abstract: Structured pruning is widely applied to compress large language models (LLMs), but its performance depends heavily on how neuron importance is estimated.
The paper presents a systematic study of how different compression techniques—pruning, quantization, and distillation—affect the capabilities of large language models (LLMs) in tasks such as mathematics, code generation, and question answering. It introduces a framework that measures capability loss and relates it to factors like model size, training stage, and compression settings, yielding simple predictive relations that generalize across unseen configurations. The authors demonstrate that sharing density responses across pruning levels can dramatically reduce the number of measurements needed, and that their predictive models closely match regression results while offering efficient decision guidance for compression method selection.
arXiv:2605. 18331v2 Announce Type: replace Abstract: Large Language Models (LLMs) have experienced significant growth and development in recent years.
arXiv:2606. 18304v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models scale compute efficiently, yet remain expensive to deploy due to their substantial memory footprint and inference overhead.
arXiv:2609.22131v1 Announce Type: cross Abstract: Structured pruning is a promising approach for reducing the substantial inference costs of Large Language Models (LLMs) while maintaining hardware ef...
arXiv:2605. 02404v2 Announce Type: replace Abstract: Model quantization has become essential for efficient large language model deployment, yet existing approaches present clear trade-offs: methods such as GPTQ and AWQ achieve practical compression but are lossy, while lossless techniques preserve fidelity but lack inference acceleration.