6 papers
To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
Wei Cheng, Yongchang Cao, Chen Shen +4
Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges f…
Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus
Huan Zhang, Wei Cheng, Wei Hu
Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly exter…
Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks
Chen Shen, Wei Cheng, Jingyue Yang +3
The proficiency of Large Language Models (LLMs) in coding tasks is often a reflection of their extensive pre-training corpora, which typically collapses when confronted with previo…
Bootstrapping Code Translation with Weighted Multilanguage Exploration
Yuhan Wu, Huan Zhang, Wei Cheng +3
Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and o…
A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement
Huan Zhang, Wei Cheng, Yuhan Wu +1
Large language models (LLMs) have achieved impressive performance on code generation. Although prior studies enhanced LLMs with prompting techniques and code refinement, they still…
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph
Wei Cheng, Xiangrong Zhu, Wei Hu
Code sharing and reuse is a widespread use practice in software engineering. Although a vast amount of open-source Python code is accessible on many online platforms, programmers o…