arXiv:2609.06557v1 Announce Type: new
Abstract: Large language models (LLMs) are often considered fragile under aggressive sparsification, and maintaining reliable performance typically requires stic...
By Hyeondo Jang, Kwanhee Lee, Dongyeop Lee, Namhoon Lee
SHIFT-LLM is a training‑free post‑pruning correction framework that inserts a Linear Residual Adapter (LRA) at each depth‑pruned site in large language models. Each LRA preserves the original residual identity while adding a lightweight affine correction calibrated via closed‑form least‑squares regression on a small held‑out set, thereby approximating the hidden state that would have been produced by the removed block. Experiments across multiple model families and benchmarks show that SHIFT‑LLM consistently recovers accuracy lost to depth pruning, achieving gains up to +15.7 points on Llama‑3.1‑8B‑Instruct with only a few hundred calibration samples and no gradient computation.
By Ali Bahri, Hang Li, Hongliang Li, Zhitang Chen
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...
By Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
The paper introduces LADDER, a method for post‑training pruning of large language models that allocates sparsity incrementally across layers to minimize end‑to‑end degradation per removed parameter. LADDER produces non‑uniform sparsity profiles and nested masks for multiple budgets, and is evaluated on five OPT, Llama, and Qwen models at 10–90% sparsity using SparseGPT. Results show that while LADDER is comparable to uniform and adaptive baselines at low sparsity, it becomes substantially more robust from 70% sparsity onward, a benefit that largely persists after posterior distillation and improves zero‑shot accuracy retention.
By Nicol\'as Mauricio Cuadrado \'Avila, Ivo Gollini Navarrete, Martin Tak\'a\v{c}, Samuel Horv\'ath
arXiv:2512.20636v2 Announce Type: replace-cross
Abstract: Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppr...
By Dhananjay Saikumar, Blesson Varghese
The paper argues that relying solely on zero‑shot task accuracy is insufficient for evaluating quantized large language models (LLMs) because accuracy ignores changes in the full predictive distribution. It proposes a distribution‑sensitive framework that measures fidelity loss by computing statistical distances—such as Jensen‑Shannon Divergence and Total Variation Distance—between the full‑vocabulary output distributions of a full‑precision BF16 reference and its quantized counterparts. Experiments across five foundation architectures and four reasoning benchmarks show that these divergence metrics increase with stronger quantization, revealing distributional drift that top‑1 accuracy fails to capture, and suggest that mixed‑precision Q4_K schemes can offer lower divergence than uniform Q4_0 at comparable memory usage.
By Shahzeb Qamar, Lorenz Sparrenberg, Christian Bauckhage, Baha Rababah, Carson Leung, Murat Kantarcioglu, Cuneyt Gurcan Akcora, Rafet Sifa