Year: 2024
Type: conference-paper
Abstract: Code Authorship Attribution (CAA) has several applications such as copyright disputes, plagiarism detection and criminal prosecution. Existing studies mainly focused on CAA by proposing machine learning (ML) and Deep Learning (DL) based techniques. The main limitations of ML-based techniques are (a) manual feature engineering is required to train these models and (b) they are vulnerable to adversarial attack. In this study, we initially fine-tune five Large Language Models (LLMs) for CAA and evaluate their performance. Our results show that LLMs are robust and less vulnerable compared to existing techniques in CAA task.
Author: Atish Dipongkor
Institution: University of Central Florida
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4403536766