1 citations · 3 across the 7 of their papers we have counts for
8 papers
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Kaifeng He, Xiaojun Zhang, Peiliang Cai +7
Large language models (LLMs) frequently generate defective code, ranging from logical bugs to security vulnerabilities. Training-data quality is one contributor to these outcomes a…
DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning
Yanlin Wang, Jiadong Wu, Tianyue Jiang +7
Large language models (LLMs) have demonstrated impressive capabilities in code generation by leveraging retrieval-augmented generation (RAG) methods. However, the computational cos…
RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories
Yanlin Wang, Ziyao Zhang, Chong Wang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. E…
A Hierarchical and Evolvable Benchmark for Fine-Grained Code Instruction Following with Multi-Turn Feedback
Guoliang Duan, Mingwei Liu, Yanlin Wang +3
Large language models (LLMs) have advanced significantly in code generation, yet their ability to follow complex programming instructions with layered and diverse constraints remai…
AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation
Kaifeng He, Mingwei Liu, Chong Wang +4
Code generation with large language models (LLMs) is highly sensitive to token selection during decoding, particularly at uncertain decision points that influence program logic. Wh…
FeedbackEval: A Benchmark for Evaluating Large Language Models in Feedback-Driven Code Repair Tasks
Dekun Dai, MingWei Liu, Anji Li +5
Code repair is a fundamental task in software development, facilitating efficient bug resolution and software maintenance. Although large language models (LLMs) have demonstrated c…
RustEvo^2: An Evolving Benchmark for API Evolution in LLM-based Rust Code Generation
Linxi Liang, Jing Gong, Mingwei Liu +5
Large Language Models (LLMs) have become pivotal tools for automating code generation in software development. However, these models face significant challenges in producing versio…
LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
Ziyao Zhang, Yanlin Wang, Chong Wang +2
Code generation aims to automatically generate code from input requirements, significantly enhancing development efficiency. Recent large language models (LLMs) based approaches ha…