11 citations · 13 across the 10 of their papers we have counts for
3 papers · 1 filter
What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces
Jordi Armengol-Estapé, Quentin Carbonneaux, Tianjun Zhang +8
Code generation and understanding are critical capabilities for large language models (LLMs). Thus, most LLMs are pretrained and fine-tuned on code data. However, these datasets ty…
Don't Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs
Chris Cummins, Volker Seeker, Jordi Armengol-Estapé +3
Tools for rewriting, refactoring and optimizing code should be fast and correct. Large language models (LLMs), by their nature, possess neither of these qualities. Yet, there remai…
Priority Sampling of Large Language Models for Compilers
Dejan Grubisic, Chris Cummins, Volker Seeker +1
Large language models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often p…