6 citations · 7 across the 4 of their papers we have counts for
4 papers
Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM
Gabriel Ryan, Siddhartha Jain, Mingyue Shang +4
Testing plays a pivotal role in ensuring software quality, yet conventional Search Based Software Testing (SBST) methods often struggle with complex software units, achieving subop…
Token Alignment via Character Matching for Subword Completion
Ben Athiwaratkun, Shiqi Wang, Mingyue Shang +7
Generative models, widely utilized in various applications, can often struggle with prompts corresponding to partial tokens. This struggle stems from tokenization, where partial to…
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…
Greener yet Powerful: Taming Large Code Generation Models with Quantization
Xiaokai Wei, Sujan Gonugondla, Wasi Ahmad +13
ML-powered code generation aims to assist developers to write code in a more productive manner, by intelligently generating code blocks based on natural language prompts. Recently,…