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Is Quantization a Deal-breaker? Empirical Insights from Large Code Models
Saima Afrin, Bowen Xu, Antonio Mastropaolo
The growing scale of large language models (LLMs) not only demands extensive computational resources but also raises environmental concerns due to their increasing carbon footprint…
A Systematic Literature Review of Parameter-Efficient Fine-Tuning for Large Code Models
Saima Afrin, Md Zahidul Haque, Antonio Mastropaolo
The rise of Artificial Intelligence (AI)-and particularly Large Language Models (LLMs) for code-has reshaped Software Engineering (SE) by enabling the automation of tasks such as c…
Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
Alessandro Giagnorio, Antonio Mastropaolo, Saima Afrin +2
Large Language Models (LLMs) have shown an impressive capability in code generation. The LLM effectiveness generally increases with its size: The higher the number of LLM's trainab…
Resource-Efficient & Effective Code Summarization
Saima Afrin, Joseph Call, Khai-Nguyen Nguyen +2
Code Language Models (CLMs) have demonstrated high effectiveness in automating software engineering tasks such as bug fixing, code generation, and code documentation. This progress…