9 citations · 12 across the 4 of their papers we have counts for
4 papers
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Yingwei Ma, Yue Yu, Shanshan Li +5
Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic m…
Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning
Mingyang Geng, Shangwen Wang, Dezun Dong +5
Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a…
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…
SSD-SSD: Communication sparsification for distributed deep learning training
Yemao Xu, Dezun Dong, Yawei Zhao +2
Intensive communication and synchronization cost for gradients and parameters is the well-known bottleneck of distributed deep learning training. Based on the observations that Syn…