Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression 事件

Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression arXiv:2606.03465v1 Announce Type: cross Abstract: Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged as a promising direction, offering compact parameterizations well suited to Transformer weight structures. However, existing studies evaluate these methods in narrow settings, leaving unclear whether tensorization is eff