2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.SE2024
Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation
Xinyu Gao, Yun Xiong, Deze Wang +4
Retrieval-augmented code generation utilizes Large Language Models as the generator and significantly expands their code generation capabilities by providing relevant code, documen…
cs.SE2023★ 1 cited
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
Deze Wang, Boxing Chen, Shanshan Li +4
As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study report…
cs.SE2021★ 2 cited
Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding
Deze Wang, Zhouyang Jia, Shanshan Li +4
With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to co…