Saved in:
| Main Authors: | Feng, Chengzhe, Sun, Yanan, Li, Ke, Zhou, Pan, Lv, Jiancheng, Lu, Aojun |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.13588 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Structure-aware Fine-tuning for Code Pre-trained Models
by: Wu, Jiayi, et al.
Published: (2024)
by: Wu, Jiayi, et al.
Published: (2024)
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
by: Chou, Jason, et al.
Published: (2025)
by: Chou, Jason, et al.
Published: (2025)
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
by: Yang, Kang, et al.
Published: (2025)
by: Yang, Kang, et al.
Published: (2025)
LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
by: Wang, Yan, et al.
Published: (2025)
by: Wang, Yan, et al.
Published: (2025)
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
by: Bi, Zhangqian, et al.
Published: (2025)
by: Bi, Zhangqian, et al.
Published: (2025)
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
by: Yang, Jian, et al.
Published: (2025)
by: Yang, Jian, et al.
Published: (2025)
Bridging Code Graphs and Large Language Models for Better Code Understanding
by: Chen, Zeqi, et al.
Published: (2025)
by: Chen, Zeqi, et al.
Published: (2025)
Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
by: Zheng, Jiasheng, et al.
Published: (2024)
by: Zheng, Jiasheng, et al.
Published: (2024)
CodeOCR: On the Effectiveness of Vision Language Models in Code Understanding
by: Shi, Yuling, et al.
Published: (2026)
by: Shi, Yuling, et al.
Published: (2026)
Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models
by: Zhang, Sheng, et al.
Published: (2023)
by: Zhang, Sheng, et al.
Published: (2023)
R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models
by: Deng, Ken, et al.
Published: (2024)
by: Deng, Ken, et al.
Published: (2024)
AutoCode: LLMs as Problem Setters for Competitive Programming
by: Zhou, Shang, et al.
Published: (2025)
by: Zhou, Shang, et al.
Published: (2025)
AutoVerus: Automated Proof Generation for Rust Code
by: Yang, Chenyuan, et al.
Published: (2024)
by: Yang, Chenyuan, et al.
Published: (2024)
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
by: Weyssow, Martin, et al.
Published: (2023)
by: Weyssow, Martin, et al.
Published: (2023)
Directional Diffusion-Style Code Editing Pre-training
by: Liang, Qingyuan, et al.
Published: (2025)
by: Liang, Qingyuan, et al.
Published: (2025)
Large Language Models for Multilingual Code Intelligence: A Survey
by: Jiang, Chao, et al.
Published: (2026)
by: Jiang, Chao, et al.
Published: (2026)
Advancing Automated In-Isolation Validation in Repository-Level Code Translation
by: Ke, Kaiyao, et al.
Published: (2025)
by: Ke, Kaiyao, et al.
Published: (2025)
Natural Is The Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
by: Wang, Yan, et al.
Published: (2024)
by: Wang, Yan, et al.
Published: (2024)
On the Effect of Token Merging on Pre-trained Models for Code
by: Saad, Mootez, et al.
Published: (2025)
by: Saad, Mootez, et al.
Published: (2025)
ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models
by: Zheng, Jiasheng, et al.
Published: (2026)
by: Zheng, Jiasheng, et al.
Published: (2026)
Leveraging Print Debugging to Improve Code Generation in Large Language Models
by: Hu, Xueyu, et al.
Published: (2024)
by: Hu, Xueyu, et al.
Published: (2024)
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
by: Zhao, Yuwei, et al.
Published: (2024)
by: Zhao, Yuwei, et al.
Published: (2024)
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
by: Mishra, Mayank, et al.
Published: (2024)
by: Mishra, Mayank, et al.
Published: (2024)
Code Fingerprints: Disentangled Attribution of LLM-Generated Code
by: Guo, Jiaxun, et al.
Published: (2026)
by: Guo, Jiaxun, et al.
Published: (2026)
Bridge and Hint: Extending Pre-trained Language Models for Long-Range Code
by: Chen, Yujia, et al.
Published: (2024)
by: Chen, Yujia, et al.
Published: (2024)
LongCodeZip: Compress Long Context for Code Language Models
by: Shi, Yuling, et al.
Published: (2025)
by: Shi, Yuling, et al.
Published: (2025)
Mercury: A Code Efficiency Benchmark for Code Large Language Models
by: Du, Mingzhe, et al.
Published: (2024)
by: Du, Mingzhe, et al.
Published: (2024)
CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation
by: Cai, Jianfeng, et al.
Published: (2026)
by: Cai, Jianfeng, et al.
Published: (2026)
Finding Compiler Bugs through Cross-Language Code Generator and Differential Testing
by: Feng, Qiong, et al.
Published: (2025)
by: Feng, Qiong, et al.
Published: (2025)
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
by: Guo, Daya, et al.
Published: (2024)
by: Guo, Daya, et al.
Published: (2024)
Is Your Benchmark (Still) Useful? Dynamic Benchmarking for Code Language Models
by: Guan, Batu, et al.
Published: (2025)
by: Guan, Batu, et al.
Published: (2025)
Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
by: Skopin, Egor, et al.
Published: (2026)
by: Skopin, Egor, et al.
Published: (2026)
Stingy Context: 18:1 Hierarchical Code Compression for LLM Auto-Coding
by: Ostby, David Linus
Published: (2026)
by: Ostby, David Linus
Published: (2026)
Calibration of Large Language Models on Code Summarization
by: Virk, Yuvraj, et al.
Published: (2024)
by: Virk, Yuvraj, et al.
Published: (2024)
CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
by: Lin, Hong Yi, et al.
Published: (2025)
by: Lin, Hong Yi, et al.
Published: (2025)
Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models
by: Gao, Shuzheng, et al.
Published: (2024)
by: Gao, Shuzheng, et al.
Published: (2024)
Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey
by: Wang, Junqiao, et al.
Published: (2024)
by: Wang, Junqiao, et al.
Published: (2024)
Mitigating Gender Bias in Code Large Language Models via Model Editing
by: Qin, Zhanyue, et al.
Published: (2024)
by: Qin, Zhanyue, et al.
Published: (2024)
AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation
by: Sun, Zhensu, et al.
Published: (2024)
by: Sun, Zhensu, et al.
Published: (2024)
Strengthening Programming Comprehension in Large Language Models through Code Generation
by: Ren, Xiaoning, et al.
Published: (2025)
by: Ren, Xiaoning, et al.
Published: (2025)
Similar Items
-
Structure-aware Fine-tuning for Code Pre-trained Models
by: Wu, Jiayi, et al.
Published: (2024) -
AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
by: Chou, Jason, et al.
Published: (2025) -
Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks
by: Yang, Kang, et al.
Published: (2025) -
LEANCODE: Understanding Models Better for Code Simplification of Pre-trained Large Language Models
by: Wang, Yan, et al.
Published: (2025) -
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
by: Bi, Zhangqian, et al.
Published: (2025)