Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Negrão, Arthur, Silva, Pedro, Freitas, Vander L. S., Moreira, Gladston, Luz, Eduardo |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility
by: Silva, Guilherme, et al.
Published: (2025)
by: Silva, Guilherme, et al.
Published: (2025)
Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
by: Oliveira, Rafael F., et al.
Published: (2024)
by: Oliveira, Rafael F., et al.
Published: (2024)
Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024)
by: Xu, Zifei, et al.
Published: (2024)
Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
by: Almeida, Pablo G., et al.
Published: (2025)
by: Almeida, Pablo G., et al.
Published: (2025)
Leveraging graph neural networks and mobility data for COVID-19 forecasting
by: Duarte, Fernando H. O., et al.
Published: (2025)
by: Duarte, Fernando H. O., et al.
Published: (2025)
Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models
by: Zhou, Chenxi, et al.
Published: (2025)
by: Zhou, Chenxi, et al.
Published: (2025)
Post Training Quantization of Large Language Models with Microscaling Formats
by: Sharify, Sayeh, et al.
Published: (2024)
by: Sharify, Sayeh, et al.
Published: (2024)
Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models
by: Pham, Cuong, et al.
Published: (2025)
by: Pham, Cuong, et al.
Published: (2025)
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
by: Lucas, Ryan, et al.
Published: (2026)
by: Lucas, Ryan, et al.
Published: (2026)
PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
by: Xiao, He, et al.
Published: (2025)
by: Xiao, He, et al.
Published: (2025)
Accumulator-Aware Post-Training Quantization for Large Language Models
by: Colbert, Ian, et al.
Published: (2024)
by: Colbert, Ian, et al.
Published: (2024)
Rethinking Output Alignment For 1-bit Post-Training Quantization of Large Language Models
by: Hoang, Dung Anh, et al.
Published: (2025)
by: Hoang, Dung Anh, et al.
Published: (2025)
Boost Post-Training Quantization via Null Space Optimization for Large Language Models
by: Zhao, Jiaqi, et al.
Published: (2025)
by: Zhao, Jiaqi, et al.
Published: (2025)
Interactions Across Blocks in Post-Training Quantization of Large Language Models
by: Shabanovi, Khasmamad, et al.
Published: (2024)
by: Shabanovi, Khasmamad, et al.
Published: (2024)
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models
by: Pham, Cuong, et al.
Published: (2025)
by: Pham, Cuong, et al.
Published: (2025)
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)
by: Xiao, Guangxuan, et al.
Published: (2022)
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression
by: Liu, Wenyuan, et al.
Published: (2024)
by: Liu, Wenyuan, et al.
Published: (2024)
A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models
by: Killian, Earl
Published: (2026)
by: Killian, Earl
Published: (2026)
CPTQuant - A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models
by: Nanda, Amitash, et al.
Published: (2024)
by: Nanda, Amitash, et al.
Published: (2024)
APTQ: Attention-aware Post-Training Mixed-Precision Quantization for Large Language Models
by: Guan, Ziyi, et al.
Published: (2024)
by: Guan, Ziyi, et al.
Published: (2024)
Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
by: Zhang, Tianao, et al.
Published: (2025)
by: Zhang, Tianao, et al.
Published: (2025)
HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
by: Yan, Xin, et al.
Published: (2026)
by: Yan, Xin, et al.
Published: (2026)
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
by: Zhang, Jingxuan, et al.
Published: (2026)
by: Zhang, Jingxuan, et al.
Published: (2026)
Training Dynamics Impact Post-Training Quantization Robustness
by: Catalan-Tatjer, Albert, et al.
Published: (2025)
by: Catalan-Tatjer, Albert, et al.
Published: (2025)
On the Plasticity and Stability for Post-Training Large Language Models
by: Qiang, Wenwen, et al.
Published: (2026)
by: Qiang, Wenwen, et al.
Published: (2026)
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices
by: Lee, Jung Hyun, et al.
Published: (2024)
by: Lee, Jung Hyun, et al.
Published: (2024)
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion Models
by: Liu, Xuewen, et al.
Published: (2024)
by: Liu, Xuewen, et al.
Published: (2024)
Scaling Law for Quantization-Aware Training
by: Chen, Mengzhao, et al.
Published: (2025)
by: Chen, Mengzhao, et al.
Published: (2025)
SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization
by: Bai, Runsheng, et al.
Published: (2024)
by: Bai, Runsheng, et al.
Published: (2024)
RepQuant: Towards Accurate Post-Training Quantization of Large Transformer Models via Scale Reparameterization
by: Li, Zhikai, et al.
Published: (2024)
by: Li, Zhikai, et al.
Published: (2024)
Improving Quantization with Post-Training Model Expansion
by: Franco, Giuseppe, et al.
Published: (2025)
by: Franco, Giuseppe, et al.
Published: (2025)
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models
by: Xiao, He, et al.
Published: (2025)
by: Xiao, He, et al.
Published: (2025)
PD-Loss: Proxy-Decidability for Efficient Metric Learning
by: Silva, Pedro, et al.
Published: (2025)
by: Silva, Pedro, et al.
Published: (2025)
SiLQ: Simple Large Language Model Quantization-Aware Training
by: Esser, Steven K., et al.
Published: (2025)
by: Esser, Steven K., et al.
Published: (2025)
Data Generation for Hardware-Friendly Post-Training Quantization
by: Dikstein, Lior, et al.
Published: (2024)
by: Dikstein, Lior, et al.
Published: (2024)
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
by: Zhao, Jiaqi, et al.
Published: (2025)
by: Zhao, Jiaqi, et al.
Published: (2025)
A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies
by: Wasswa, Hassan, et al.
Published: (2025)
by: Wasswa, Hassan, et al.
Published: (2025)
Group Causal Policy Optimization for Post-Training Large Language Models
by: Gu, Ziyin, et al.
Published: (2025)
by: Gu, Ziyin, et al.
Published: (2025)
Optimizing Large Language Model Training Using FP4 Quantization
by: Wang, Ruizhe, et al.
Published: (2025)
by: Wang, Ruizhe, et al.
Published: (2025)
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
by: Arai, Yamato, et al.
Published: (2025)
by: Arai, Yamato, et al.
Published: (2025)
Similar Items
-
A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility
by: Silva, Guilherme, et al.
Published: (2025) -
Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
by: Oliveira, Rafael F., et al.
Published: (2024) -
Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024) -
Deep Learning for School Dropout Detection: A Comparison of Tabular and Graph-Based Models for Predicting At-Risk Students
by: Almeida, Pablo G., et al.
Published: (2025) -
Leveraging graph neural networks and mobility data for COVID-19 forecasting
by: Duarte, Fernando H. O., et al.
Published: (2025)