7 papers
Think Anywhere in Code Generation
Xue Jiang, Tianyu Zhang, Ge Li +8
Recent advances in reasoning Large Language Models (LLMs) have primarily relied on upfront thinking, where reasoning occurs before final answer. However, this approach suffers from…
KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?
Xue Jiang, Ge Li, Jiaru Qian +12
Large language models (LLMs) excel at general programming but struggle with domain-specific software development, necessitating domain specialization methods for LLMs to learn and…
CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Xue Jiang, Yihong Dong, Mengyang Liu +10
While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and th…
Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging
Ziyue Hua, Tianyu Chen, Yeyun Gong +8
Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-base…
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…
ROCODE: Integrating Backtracking Mechanism and Program Analysis in Large Language Models for Code Generation
Xue Jiang, Yihong Dong, Yongding Tao +4
Large language models (LLMs) have achieved impressive performance in code generation recently, offering programmers revolutionary assistance in software development. However, due t…