60 citations · 84 across the 5 of their papers we have counts for
5 papers
"I Would Have Written My Code Differently'': Beginners Struggle to Understand LLM-Generated Code
Yangtian Zi, Luisa Li, Arjun Guha +2
Large language models (LLMs) are being increasingly adopted for programming work. Prior work shows that while LLMs accelerate task completion for professional programmers, beginnin…
Substance Beats Style: Why Beginning Students Fail to Code with LLMs
Francesca Lucchetti, Zixuan Wu, Arjun Guha +2
Although LLMs are increasing the productivity of professional programmers, existing work shows that beginners struggle to prompt LLMs to solve text-to-code tasks. Why is this the c…
How Beginning Programmers and Code LLMs (Mis)read Each Other
Sydney Nguyen, Hannah McLean Babe, Yangtian Zi +3
Generative AI models, specifically large language models (LLMs), have made strides towards the long-standing goal of text-to-code generation. This progress has invited numerous stu…
StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
Hannah McLean Babe, Sydney Nguyen, Yangtian Zi +3
Code LLMs are being rapidly deployed and there is evidence that they can make professional programmers more productive. Current benchmarks for code generation measure whether model…
MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation
Federico Cassano, John Gouwar, Daniel Nguyen +10
Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code gene…