Understanding Calibration and Truncation Error Propagation in Training-Free Low-Rank Compression for LLMs

arXiv:2608.08506 · cs.AI, cs.CL, cs.PF · Submitted 2026-08-09 · Read on arXiv

Mohanad Odema, Gabrielle De Micheli, Dayin Gou, Nilesh Malpeddi, Prathamesh Vaste, Jacob Song

cs.AI, cs.CL, cs.PF

Submitted: 2026-08-09

Updated: 2026-08-11

Comments: COLM 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 79× in this paper · explained in 7 episodes
• Accuracy — 26× in this paper · explained in 1 episode
• Quantization — 16× in this paper · explained in 12 episodes
• Pruning — 14× in this paper · explained in 4 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Iterative Refinement — 6× in this paper · explained in 3 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Block Influence — 5× in this paper
• Sparsity — 5× in this paper · explained in 4 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Reconstruction Error — 4× in this paper · explained in 2 episodes
• Singular Value Decomposition — 4× in this paper
• Perplexity — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Elias — 2× in this paper · explained in 2 episodes
• Singular Value Decomposition (SVD) — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Activation Sparsity — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Multi-Head Self Attention — 1× in this paper
• Multi-Layer Perceptron — 1× in this paper
• Normalized Mean Squared Error — 1× in this paper
• Robustness — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• ERC-SVD: Error-Controlled SVD for Large Language Model Compression
• Gemini: A Family of Highly Capable Multimodal Models
• The Llama 3 Herd of Models
• Optimal Brain Restoration for Joint Quantization and Sparsification of LLMs
• SlimLLM: Accurate Structured Pruning for Large Language Models
• LatentLLM: Attention-Aware Joint Tensor Compression
• AdaSVD: Adaptive Singular Value Decomposition for Large Language Models
• EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Qwen3 Technical Report
• ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models

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