65 citations · 136 across the 30 of their papers we have counts for
6 papers · 1 filter
Can Programming Languages Boost Each Other via Instruction Tuning?
Daoguang Zan, Ailun Yu, Bo Shen +8
When human programmers have mastered a programming language, it would be easier when they learn a new programming language. In this report, we focus on exploring whether programmin…
Private-Library-Oriented Code Generation with Large Language Models
Daoguang Zan, Bei Chen, Yongshun Gong +6
Large language models (LLMs), such as Codex and GPT-4, have recently showcased their remarkable code generation abilities, facilitating a significant boost in coding efficiency. Th…
PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback
Bo Shen, Jiaxin Zhang, Taihong Chen +9
Large Language Models for Code (Code LLM) are flourishing. New and powerful models are released on a weekly basis, demonstrating remarkable performance on the code generation task.…
Uncovering and Quantifying Social Biases in Code Generation
Yan Liu, Xiaokang Chen, Yan Gao +6
With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the socia…
Hierarchical and Contrastive Representation Learning for Knowledge-aware Recommendation
Bingchao Wu, Yangyuxuan Kang, Daoguang Zan +2
Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagat…
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Fengji Zhang, Bei Chen, Yue Zhang +6
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository. While for automated code completion tools, it…