10 citations · 19 across the 9 of their papers we have counts for
9 papers
Improving Natural Language Capability of Code Large Language Model
Wei Li, Daoguang Zan, Bei Guan +3
Code large language models (Code LLMs) have demonstrated remarkable performance in code generation. Nonetheless, most existing works focus on boosting code LLMs from the perspectiv…
A GAN-based data poisoning framework against anomaly detection in vertical federated learning
Xiaolin Chen, Daoguang Zan, Wei Li +2
In vertical federated learning (VFL), commercial entities collaboratively train a model while preserving data privacy. However, a malicious participant's poisoning attack may degra…
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…
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…
Large Language Models Meet NL2Code: A Survey
Daoguang Zan, Bei Chen, Fengji Zhang +5
The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence. Thanks to the rapid developmen…
When Language Model Meets Private Library
Daoguang Zan, Bei Chen, Zeqi Lin +3
With the rapid development of pre-training techniques, a number of language models have been pre-trained on large-scale code corpora and perform well in code generation. In this pa…
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation
Daoguang Zan, Bei Chen, Dejian Yang +6
Code generation is a longstanding challenge, aiming to generate a code snippet based on a natural language description. Usually, expensive text-code paired data is essential for tr…
Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning
Xiaolin Chen, Shuai Zhou, Bei guan +4
The increasing concerns about data privacy and security drive an emerging field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning…