1 citations · 1 across the 1 of their papers we have counts for
5 papers
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Code Smell Detection
Beiqi Zhang, Peng Liang, Xin Zhou +5
Automated code smell detection faces persistent challenges due to the subjectivity of heuristic rules and the limited performance of traditional ML/DL models. While Large Language…
Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi +5
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent adv…
CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences
Martin Weyssow, Aton Kamanda, Xin Zhou +1
Evaluating the alignment of large language models (LLMs) with user-defined coding preferences is a challenging endeavour that requires a deep assessment of LLMs' outputs. Existing…
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
Martin Weyssow, Xin Zhou, Kisub Kim +2
Large language models (LLMs) demonstrate impressive capabilities to generate accurate code snippets given natural language intents in a zero-shot manner, i.e., without the need for…
Large Language Model for Vulnerability Detection and Repair: Literature Review and the Road Ahead
Xin Zhou, Sicong Cao, Xiaobing Sun +1
The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerabili…