3 citations · 4 across the 15 of their papers we have counts for
6 papers · 1 filter
VulInstruct: Teaching LLMs Root-Cause Reasoning for Vulnerability Detection via Security Specifications
Hao Zhu, Jia Li, Cuiyun Gao +7
Large language models (LLMs) have achieved remarkable progress in code understanding tasks. However, they demonstrate limited performance in vulnerability detection and struggle to…
VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection
Ziliang Wang, Ge Li, Jia Li +2
The application of language models to project-level vulnerability detection remains challenging, owing to the dual requirement of accurately localizing security-sensitive code and…
Computational Thinking Reasoning in Large Language Models
Kechi Zhang, Ge Li, Jia Li +8
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as div…
SATURN: SAT-based Reinforcement Learning to Unleash LLMs Reasoning
Huanyu Liu, Ge Li, Jia Li +3
How to design reinforcement learning (RL) tasks that effectively unleash the reasoning capability of large language models (LLMs) remains an open question. Existing RL tasks (e.g.,…
aiXcoder-7B-v2: Training LLMs to Fully Utilize the Long Context in Repository-level Code Completion
Jia Li, Hao Zhu, Huanyu Liu +7
Large Language Models (LLMs) have shown promising results in repository-level code completion, which completes code based on the in-file and cross-file context of a repository. The…
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
Yihong Dong, Ge Li, Xue Jiang +8
Periodicity, as one of the most important basic characteristics, lays the foundation for facilitating structured knowledge acquisition and systematic cognitive processes within hum…