arXiv:2608. 08188v1 Announce Type: new Abstract: Post-training quantization reduces the deployment cost of large language models, yet how severely a quantized model degrades is not determined by bit-width alone.
By Chenxi Zhou, Pengfei Cao, Jinyu Ye, Bohan Yu, Haida Yu, Jiang Li, Jun Zhao, Kang Liu
arXiv:2607. 08734v1 Announce Type: new Abstract: Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively on accuracy and perplexity.
By Baha Rababah, Cuneyt Gurcan Akcora, Carson K. Leung
Post-training quantization is widely used to deploy large language models in resource-constrained settings, yet its evaluation relies almost exclusively on accuracy and perplexity. We show that these metrics fail to capture behavioral changes induced by quantization.
Post‑training quantization compresses large language models by storing weights at reduced precision, introducing errors into hidden states that could accumulate with depth. However, pretrained models accumulate far less hidden‑state error than randomly initialized ones, largely preserving downstream performance. The study identifies two key mechanisms: (1) each layer’s new error tends to oppose inherited error, partially canceling it, and (2) the LM‑head geometry preserves high‑rank token scores, mitigating output changes.
By Yuxiang Chen, Michael Beyer, Jun Zhu, Jianfei Chen
arXiv:2606. 04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks.
By Pasindu Wickramasinghe, Achyuta Muthuvelan, Rachmad Vidya Wicaksana Putra, Minghao Shao, Muhammad Shafique
The paper investigates where post‑training quantization (PTQ) harms large language models (LLMs) and how to best allocate a limited precision budget. By causally raising each layer to 8‑bit precision across nine open‑weight models, the authors find that quantization damage is diffuse rather than concentrated in specific task circuits or weight statistics, and that globally refining quantization granularity outperforms selectively protecting the most recoverable layers. They also observe that the residual accuracy loss is budget‑limited and that peak recovery locations correlate with architecture within families but not across families.
By Jundong Hu, Shekar Ramachandran