6 papers
What Was Once Learned May Need to Be Unlearned: Machine Unlearning for Deprecated API Knowledge in Large Language Models
Jin Liu, Yanzhong He, Guancheng Lin +4
Large language models (LLMs) for code completion may generate deprecated APIs because their pre-training corpora contain code from historical library versions. Existing approaches…
When Models Meet Users: An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
Yujian Liu, Xiao Yu, Jacky Keung +3
Large language models (LLMs) have rapidly evolved from general-purpose systems to multimodal models capable of processing text, images, and audio. As both general-purpose LLMs (GLL…
R2ComSync: Improving Code-Comment Synchronization with In-Context Learning and Reranking
Zhen Yang, Hongyi Lin, Xiao Yu +5
Code-Comment Synchronization (CCS) aims to synchronize the comments with code changes in an automated fashion, thereby significantly reducing the workload of developers during soft…
Practitioners' Expectations on Log Anomaly Detection
Xiaoxue Ma, Yishu Li, Jacky Keung +5
Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over th…
HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks
Fengji Zhang, Linquan Wu, Huiyu Bai +6
Understanding and reasoning over diagrams is a fundamental aspect of human intelligence. While Large Multimodal Models (LMMs) have demonstrated impressive capabilities across vario…
On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations
Xiaoxue Ma, Huiqi Zou, Pinjia He +4
Numerous Deep Learning (DL)-based approaches have gained attention in software Log Anomaly Detection (LAD), yet class imbalance in training data remains a challenge, with anomalies…