6 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 6 cited
NExT: Teaching Large Language Models to Reason about Code Execution
Ansong Ni, Miltiadis Allamanis, Arman Cohan +4
A fundamental skill among human developers is the ability to understand and reason about program execution. As an example, a programmer can mentally simulate code execution in natu…
cs.LG2024★ 2 cited
Grounding Data Science Code Generation with Input-Output Specifications
Yeming Wen, Pengcheng Yin, Kensen Shi +3
Large language models (LLMs) have recently demonstrated a remarkable ability to generate code from natural language (NL) prompts. However, in the real world, NL is often too ambigu…
cs.LG2022★ 1 cited
A Library for Representing Python Programs as Graphs for Machine Learning
David Bieber, Kensen Shi, Petros Maniatis +4
Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static…