5 papers
Mitigating Package Hallucinations in Large Language Models via Model Editing
Shuhan Liu, Yukai Zhao, Xing Hu +3
Large language models (LLMs) have demonstrated strong capabilities in software engineering tasks, such as code generation, library recommendation, and dependency configuration. How…
RACE-Bench: A Reasoning-Augmented Benchmark for Repository-Level Code Agents on Feature Addition
Shuhan Liu, Zhiyi Zhao, Xing Hu +3
Repository-level code agents have shown strong promise in real-world feature addition tasks, making reliable evaluation of their capabilities increasingly important. However, exist…
Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework
Kerui Huang, Shuhan Liu, Xing Hu +3
Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by prompting intermediate steps, improving accuracy and robustness in arithmetic, logic, and commonsense task…
CREME: Robustness Enhancement of Code LLMs via Layer-Aware Model Editing
Shuhan Liu, Xing Hu, Kerui Huang +3
Large language models (LLMs) have demonstrated impressive capabilities in code generation, where the natural language prompt plays a crucial role in conveying user intent to the mo…
An Empirical Study of Vulnerable Package Dependencies in LLM Repositories
Shuhan Liu, Xing Hu, Xin Xia +2
Large language models (LLMs) have developed rapidly in recent years, revolutionizing various fields. Despite their widespread success, LLMs heavily rely on external code dependenci…