7 citations · 44 across the 40 of their papers we have counts for
17 papers · 1 filter
Exploring the Potential of Diffusion Large Language Models in Code Generation
Chengze Li, Yitong Zhang, Jia Li +2
LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code token-by-token from left to right. Howeve…
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…
GramTrans: A Better Code Representation Approach in Code Generation
Zhao Zhang, Qingyuan Liang, Zeyu Sun +6
Code generation has shown great promise in assisting software development. A fundamental yet underexplored question is how the choice of code representation affects model performan…
Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
Zhihong Sun, Jia Li, Yao Wan +7
Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models (LLMs) have shown promising capabiliti…
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…
StackTrans: From Large Language Model to Large Pushdown Automata Model
Kechi Zhang, Ge Li, Jia Li +4
The Transformer architecture has emerged as a landmark advancement within the broad field of artificial intelligence, effectively catalyzing the advent of large language models (LL…
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…
Large Language Model Unlearning for Source Code
Xue Jiang, Yihong Dong, Huangzhao Zhang +9
While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringe…
DAVSP: Safety Alignment for Large Vision-Language Models via Deep Aligned Visual Safety Prompt
Yitong Zhang, Jia Li, Liyi Cai +1
Large Vision-Language Models (LVLMs) have achieved impressive progress across various applications but remain vulnerable to malicious queries that exploit the visual modality. Exis…
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.,…
GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation
Jia Li, Xianjie Shi, Kechi Zhang +10
Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress for code generation. Recently, large…
SEAlign: Alignment Training for Software Engineering Agent
Kechi Zhang, Huangzhao Zhang, Ge Li +4
Recent advances in code generation models have demonstrated impressive capabilities in automating software development tasks, yet these models still struggle in real-world software…
Uncertainty-Guided Chain-of-Thought for Code Generation with LLMs
Yuqi Zhu, Ge Li, Xue Jiang +4
Chain-of-Thought (CoT) reasoning has been demonstrated as an effective technique for improving the problem-solving capabilities of large language models (LLMs) in the context of co…
LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding
Jia Li, Xuyuan Guo, Lei Li +7
Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by…
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…
Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points
Kechi Zhang, Ge Li, Jia Li +2
Code generation models have shown significant potential for automating programming tasks. However, the challenge of generating accurate and reliable code persists due to the highly…