200 citations · 336 across the 9 of their papers we have counts for
3 papers · 1 filter
Agnostics: Learning to Code in Any Programming Language via Reinforcement with a Universal Learning Environment
Aleksander Boruch-Gruszecki, Yangtian Zi, Zixuan Wu +4
Large language models (LLMs) already excel at writing code in high-resource languages such as Python and JavaScript, yet stumble on low-resource languages that remain essential to…
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…