Francesco Tudisco

Paper accepted @ LREC 2026

Our paper Low-Rank Compression of Language Models via Differentiable Rank Selection has been accepted at LREC 2026!

Joint work with Sidhant Sundrani and Pasquale Minervini. We propose LLRC, a gradient-based method for optimally selecting per-layer ranks when compressing large language models via low-rank decomposition, consistently outperforming competing rank-selection methods across various compression rates on reasoning and QA benchmarks.