Cover Image

[Replication Package] Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Giagnorio, Alessandro, Mastropaolo, Antonio, Afrin, Saima, Di Penta, Massimiliano, BAVOTA, Gabriele
Format:	Recurso digital
Published:	Zenodo 2025
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
by: Giagnorio, Alessandro, et al.
Published: (2025)

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models
by: Afrin, Saima, et al.
Published: (2025)

Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization
by: Midolo, Alessandro, et al.
Published: (2026)

Not All Tokens Matter: Data-Centric Optimization for Efficient Code Summarization
by: Afrin, Saima, et al.
Published: (2026)

How the Training Procedure Impacts the Performance of Deep Learning-based Vulnerability Patching
by: Mastropaolo, Antonio, et al.
Published: (2024)

A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models
by: Afrin, Saima, et al.
Published: (2025)

Automated Refactoring of Non-Idiomatic Python Code: A Differentiated Replication with LLMs
by: Midolo, Alessandro, et al.
Published: (2025)

Parameter-Efficient Multi-Task Fine-Tuning in Code-Related Tasks
by: Haque, Md Zahidul, et al.
Published: (2026)

Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?
by: Vitale, Antonio, et al.
Published: (2025)

A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems
by: Pepe, Federica, et al.
Published: (2024)

Enhancing Code Generation for Low-Resource Languages: No Silver Bullet
by: Giagnorio, Alessandro, et al.
Published: (2025)

Resource-Efficient & Effective Code Summarization
by: Afrin, Saima, et al.
Published: (2025)

Unveiling ChatGPT's Usage in Open Source Projects: A Mining-based Study
by: Tufano, Rosalia, et al.
Published: (2024)

Developers and Generative AI: A Study of Self-Admitted Usage in Open Source Projects
by: Tufano, Rosalia, et al.
Published: (2026)

Why Personalizing Deep Learning-Based Code Completion Tools Matters
by: Giagnorio, Alessandro, et al.
Published: (2025)

From Human to Machine Refactoring: Assessing GPT-4's Impact on Python Class Quality and Readability
by: Midolo, Alessandro, et al.
Published: (2026)

Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation
by: Fang, Sen, et al.
Published: (2025)

An Empirical Study on the Effects of System Prompts in Instruction-Tuned Models for Code Generation
by: Cheng, Zaiyu, et al.
Published: (2026)

CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
by: Bifolco, Daniele, et al.
Published: (2025)

Towards Summarizing Code Snippets Using Pre-Trained Transformers
by: Mastropaolo, Antonio, et al.
Published: (2024)

Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot
by: Bifolco, Daniele, et al.
Published: (2025)

On the Effectiveness of LLM-as-a-judge for Code Generation and Summarization
by: Crupi, Giuseppe, et al.
Published: (2025)

Code Review Automation: Strengths and Weaknesses of the State of the Art
by: Tufano, Rosalia, et al.
Published: (2024)

Accumulator-Aware Post-Training Quantization for Large Language Models
by: Colbert, Ian, et al.
Published: (2024)

Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024)

Post Training Quantization of Large Language Models with Microscaling Formats
by: Sharify, Sayeh, 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)

PTQTP: Post-Training Quantization to Trit-Planes for Large Language Models
by: Xiao, He, et al.
Published: (2025)

Interactions Across Blocks in Post-Training Quantization of Large Language Models
by: Shabanovi, Khasmamad, et al.
Published: (2024)

Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks
by: Nyamsuren, Enkhbold
Published: (2024)

Prompt-Driven Code Summarization: A Systematic Literature Review
by: Farjana, Afia, et al.
Published: (2026)

An Evaluation Framework for LLM-Driven Regulatory-to-Policy-as-Code Translation - Replication Package
by: Nguyen, Bao Van
Published: (2026)

Evaluating the Impact of Post-Training Quantization on Reliable VQA with Multimodal LLMs
by: Kurz, Paul Jonas, et al.
Published: (2026)

Adaptive Layer-Wise Transformations for Post-Training Quantization of Large Language Models
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)

Training Dynamics Impact Post-Training Quantization Robustness
by: Catalan-Tatjer, Albert, et al.
Published: (2025)

Augmenting Software Bills of Materials with Software Vulnerability Description: A Preliminary Study on GitHub
by: Fucci, Davide, et al.
Published: (2025)

Machine Learning in the Wild: Early Evidence of Non-Compliant ML-Automation in Open-Source Software
by: Arshid, Zohaib, et al.
Published: (2026)

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
by: Lucas, Ryan, et al.
Published: (2026)

Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients
by: Xiang, Ziwei, et al.
Published: (2026)