1 citations · 1 across the 2 of their papers we have counts for
4 papers
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
Zhangqian Bi, Yao Wan, Zhaoyang Chu +5
Pre-training a language model and then fine-tuning it has shown to be an efficient and effective technique for a wide range of code intelligence tasks, such as code generation, cod…
CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code
Batu Guan, Yao Wan, Zhangqian Bi +4
Large Language Models (LLMs) have achieved remarkable progress in code generation. It now becomes crucial to identify whether the code is AI-generated and to determine the specific…
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
Zhangqian Bi, Yao Wan, Zheng Wang +7
Large Language Models (LLMs) have shown remarkable progress in automated code generation. Yet, LLM-generated code may contain errors in API usage, class, data structure, or missing…
Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit
Yao Wan, Yang He, Zhangqian Bi +6
Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and pr…