1 citations · 1 across the 2 of their papers we have counts for
4 papers
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Cuiyun Gao, Guodong Fan, Chun Yong Chong +5
Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly int…
DemoTuner: Automatic Performance Tuning for Database Management Systems Based on Demonstration Reinforcement Learning
Hui Dou, Lei Jin, Yuxuan Zhou +3
The performance of modern DBMSs such as MySQL and PostgreSQL heavily depends on the configuration of performance-critical knobs. Manual tuning these knobs is laborious and ineffici…
DaiFu: In-Situ Crash Recovery for Deep Learning Systems
Zilong He, Pengfei Chen, Hongyu Zhang +4
Deep learning (DL) systems have been widely adopted in many areas, and are becoming even more popular with the emergence of large language models. However, due to the complex softw…
InferLog: Accelerating LLM Inference for Online Log Parsing via ICL-oriented Prefix Caching
Yilun Wang, Pengfei Chen, Haiyu Huang +5
Modern software systems generate massive volumes of runtime logs, necessitating efficient and accurate log parsing to enable critical downstream tasks such as anomaly detection and…