16 citations · 88 across the 40 of their papers we have counts for
7 papers · 1 filter
DependEval: Benchmarking LLMs for Repository Dependency Understanding
Junjia Du, Yadi Liu, Hongcheng Guo +4
While large language models (LLMs) have shown considerable promise in code generation, real-world software development demands advanced repository-level reasoning. This includes un…
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation
Kaiwen Yan, Hongcheng Guo, Xuanqing Shi +3
With the rapid advancement of Large Language Models (LLMs), the demand for robust instruction-following capabilities in code generation tasks has grown significantly. Code generati…
ADC: Enhancing Function Calling Via Adversarial Datasets and Code Line-Level Feedback
Wei Zhang, Yi Zhang, Li Zhu +5
Large Language Models (LLMs) have made significant strides in Natural Language Processing and coding, yet they struggle with robustness and accuracy in complex function calls. To t…
ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing
Wei Zhang, Xianfu Cheng, Yi Zhang +8
Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks…
Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging
Wei Zhang, Xiangyuan Guan, Lu Yunhong +5
Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults.…
MLAD: A Unified Model for Multi-system Log Anomaly Detection
Runqiang Zang, Hongcheng Guo, Jian Yang +6
In spite of the rapid advancements in unsupervised log anomaly detection techniques, the current mainstream models still necessitate specific training for individual system dataset…
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction
Hongcheng Guo, Yuhui Guo, Renjie Chen +7
Fully supervised log anomaly detection methods suffer the heavy burden of annotating massive unlabeled log data. Recently, many semi-supervised methods have been proposed to reduce…