Cover Image

On the Impact of Calibration Data in Post-training Quantization and Pruning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Williams, Miles, Aletras, Nikolaos
Format:	Preprint
Published:	2023
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Self-calibration for Language Model Quantization and Pruning
by: Williams, Miles, et al.
Published: (2024)

Vocabulary-level Memory Efficiency for Language Model Fine-tuning
by: Williams, Miles, et al.
Published: (2023)

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
by: Chrysostomou, George, et al.
Published: (2023)

Compressing Language Models for Specialized Domains
by: Williams, Miles, et al.
Published: (2025)

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
by: Yamaguchi, Atsuki, et al.
Published: (2026)

Where does output diversity collapse in post-training?
by: Karouzos, Constantinos, et al.
Published: (2026)

Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models
by: Zhao, Zhixue, et al.
Published: (2024)

OAC: Output-adaptive Calibration for Accurate Post-training Quantization
by: Edalati, Ali, et al.
Published: (2024)

Incorporating Attribution Importance for Improving Faithfulness Metrics
by: Zhao, Zhixue, et al.
Published: (2023)

Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?
by: Lewis-Lim, Samuel, et al.
Published: (2025)

Enhancing Data Quality through Simple De-duplication: Navigating Responsible Computational Social Science Research
by: Mu, Yida, et al.
Published: (2024)

How Private are Language Models in Abstractive Summarization?
by: Hughes, Anthony, et al.
Published: (2024)

Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance
by: Alajrami, Ahmed, et al.
Published: (2025)

Progressive Depth Up-scaling via Optimal Transport
by: Cao, Mingzi, et al.
Published: (2025)

Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
by: Villegas, Danae Sánchez, et al.
Published: (2023)

Adapting Chat Language Models Using Only Target Unlabeled Language Data
by: Yamaguchi, Atsuki, et al.
Published: (2024)

How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
by: Yamaguchi, Atsuki, et al.
Published: (2024)

An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference
by: Yamaguchi, Atsuki, et al.
Published: (2024)

Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets
by: Mu, Yida, et al.
Published: (2023)

GreekBarBench: A Challenging Benchmark for Free-Text Legal Reasoning and Citations
by: Chlapanis, Odysseas S., et al.
Published: (2025)

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift
by: Karouzos, Constantinos, et al.
Published: (2026)

Deconstructing Attention: Investigating Design Principles for Effective Language Modeling
by: Xue, Huiyin, et al.
Published: (2025)

Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?
by: Lewis-Lim, Samuel, et al.
Published: (2025)

We Need to Talk About Classification Evaluation Metrics in NLP
by: Vickers, Peter, et al.
Published: (2024)

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
by: Yamaguchi, Atsuki, et al.
Published: (2025)

Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
by: Bandari, Abhinav, et al.
Published: (2024)

Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision
by: Tan, Xingwei, et al.
Published: (2025)

The Uneven Impact of Post-Training Quantization in Machine Translation
by: Marie, Benjamin, et al.
Published: (2025)

PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
by: Hughes, Anthony, et al.
Published: (2025)

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs
by: Lin, Haokun, et al.
Published: (2025)

Efficient Post-training Quantization with FP8 Formats
by: Shen, Haihao, et al.
Published: (2023)

On the Limitations of Language Targeted Pruning: Investigating the Calibration Language Impact in Multilingual LLM Pruning
by: Kurz, Simon, et al.
Published: (2024)

Who is bragging more online? A large scale analysis of bragging in social media
by: Jin, Mali, et al.
Published: (2024)

AdpQ: A Zero-shot Calibration Free Adaptive Post Training Quantization Method for LLMs
by: Ghaffari, Alireza, et al.
Published: (2024)

Beware of Calibration Data for Pruning Large Language Models
by: Ji, Yixin, et al.
Published: (2024)

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
by: Villegas, Danae Sánchez, et al.
Published: (2026)

Boundary-targeted Membership Inference Attacks on Safety Classifiers
by: Hughes, Anthony, et al.
Published: (2026)

Think Before You Prune: Selective Self-Generated Calibration for Pruning Large Reasoning Models
by: Xiang, Yang, et al.
Published: (2025)

Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models
by: Cao, Mingzi, et al.
Published: (2026)

PASER: Post-Training Data Selection for Efficient Pruned Large Language Model Recovery
by: He, Bowei, et al.
Published: (2025)