arXiv Machine Learning By Zishan Shao, Lixun Zhang, Kangning Cui, Wenhao Wu, Jinhee Kim, Yixiao Wang, Ting Jiang, Hancheng Ye, Qinsi Wang, Fan Yang, Danyang Zhuo, Yiran Chen, Hai Li

LowRankArena: A Standardized Evaluation Platform for SVD-Based LLM Compression

Read the original on arXiv Machine Learning →

LowRankArena is a standardized evaluation platform for SVD‑based low‑rank compression of large language models, unifying task versions, compression budgets, comparison regimes, and inference measurements. It provides a reproducible pipeline with over 3 TiB of released compressed checkpoints, enabling consistent comparisons across methods. An audit of five representative SVD techniques using LowRankArena shows that prior reported gains are highly conditional, with performance leaders and tiers shifting across backbones and keep ratios, and that nominal low‑rank savings often yield limited end‑to‑end speedups.

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arXiv AI
Oct 28

SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping

SkipCat introduces a rank‑maximized low‑rank compression framework for large language models, combining intra‑layer shared low‑rank projections and block‑skipping techniques to reduce redundancy and computational overhead. By allowing higher retained ranks while maintaining the same compression rate, SkipCat achieves a 7% accuracy improvement on zero‑shot tasks compared to prior low‑rank methods, all without additional fine‑tuning.

By Yu-Chen Lu, Sheng-Feng Yu, Hui-Hsien Weng, Pei-Shuo Wang, Yu-Fang Hu, Liang Hung-Chun, Hung-Yueh Chiang, Kai-Chiang Wu