200 citations · 336 across the 9 of their papers we have counts for
4 papers · 1 filter
AgentPack: A Dataset of Code Changes, Co-Authored by Agents and Humans
Yangtian Zi, Zixuan Wu, Aleksander Boruch-Gruszecki +2
Fine-tuning large language models for code editing has typically relied on mining commits and pull requests. The working hypothesis has been that commit messages describe human int…
More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generation
Yangtian Zi, Harshitha Menon, Arjun Guha
State-of-the-art Large Language Models (LLMs) achieve high pass@1 on general benchmarks like HumanEval but underperform on specialized suites such as ParEval. Is this due to LLMs m…
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…
"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…