9 citations · 13 across the 7 of their papers we have counts for
7 papers
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3
Reinforcement learning (RL) from unit-test feedback has become a standard post-training recipe for improving large language models (LLMs) on code generation. However, the pass-all-…
ACES: Who Tests the Tests? Leave-One-Out AUC Consistency for Code Generation
Hui Sun, Yun-Ji Zhang, Zheng Xie +4
Selecting LLM-generated code candidates using LLM-generated tests is challenging because the tests themselves may be incorrect. Existing methods either treat all tests equally or r…
Mitigating Negative Transfer via Reducing Environmental Disagreement
Hui Sun, Zheng Xie, Hao-Yuan He +1
Unsupervised Domain Adaptation~(UDA) focuses on transferring knowledge from a labeled source domain to an unlabeled target domain, addressing the challenge of \emph{domain shift}.…
Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking
Zhi-Cun Lyu, Xin-Ye Li, Zheng Xie +1
Code generation has been greatly enhanced by the profound advancements in Large Language Models (LLMs) recently. Nevertheless, such LLM-based code generation approaches still strug…
Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation
Xin-Ye Li, Jiang-Tian Xue, Zheng Xie +1
Code generation aims to automatically generate source code from high-level task specifications, which can significantly increase productivity of software engineering. Recently, app…
AUC Optimization from Multiple Unlabeled Datasets
Zheng Xie, Yu Liu, Ming Li
Weakly supervised learning aims to empower machine learning when the perfect supervision is unavailable, which has drawn great attention from researchers. Among various types of we…
Weakly Supervised AUC Optimization: A Unified Partial AUC Approach
Zheng Xie, Yu Liu, Hao-Yuan He +2
Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to…