29 citations · 77 across the 31 of their papers we have counts for
5 papers · 1 filter
aiXcoder-7B: A Lightweight and Effective Large Language Model for Code Processing
Siyuan Jiang, Jia Li, He Zong +11
Large Language Models (LLMs) have been widely used in code completion, and researchers are focusing on scaling up LLMs to improve their accuracy. However, larger LLMs have lower in…
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code
Kechi Zhang, Ge Li, Yihong Dong +5
Code generation models have shown significant potential for programming tasks. However, existing training methods like supervised fine-tuning face key limitations: they do not effe…
FAN: Fourier Analysis Networks
Yihong Dong, Ge Li, Yongding Tao +7
Despite the remarkable successes of general-purpose neural networks, such as MLPs and Transformers, we find that they exhibit notable shortcomings in modeling and reasoning about p…
HiRoPE: Length Extrapolation for Code Models Using Hierarchical Position
Kechi Zhang, Ge Li, Huangzhao Zhang +1
Addressing the limitation of context length in large language models for code-related tasks is the primary focus of this paper. Existing LLMs are constrained by their pre-trained c…
CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges
Kechi Zhang, Jia Li, Ge Li +2
Large Language Models (LLMs) have shown promise in automated code generation but typically excel only in simpler tasks such as generating standalone code units. Real-world software…