18 citations · 50 across the 11 of their papers we have counts for
5 papers · 1 filter
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning
Bingchang Liu, Chaoyu Chen, Cong Liao +9
Code LLMs have emerged as a specialized research field, with remarkable studies dedicated to enhancing model's coding capabilities through fine-tuning on pre-trained models. Previo…
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
Ziyin Zhang, Chaoyu Chen, Bingchang Liu +5
In this work we systematically review the recent advancements in software engineering with language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 relate…
Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System
Jun Huang, Yang Yang, Hang Yu +2
Microservice architecture has sprung up over recent years for managing enterprise applications, due to its ability to independently deploy and scale services. Despite its benefits,…
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable Basis
Zelin Ni, Hang Yu, Shizhan Liu +2
Bases have become an integral part of modern deep learning-based models for time series forecasting due to their ability to act as feature extractors or future references. To be ef…
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
Peng Di, Jianguo Li, Hang Yu +35
Code Large Language Models (Code LLMs) have gained significant attention in the industry due to their wide applications in the full lifecycle of software engineering. However, the…