29 citations · 34 across the 3 of their papers we have counts for
3 papers
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…
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Naman Jain, King Han, Alex Gu +7
Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as ne…
CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
Alex Gu, Baptiste Rozière, Hugh Leather +3
We present CRUXEval (Code Reasoning, Understanding, and eXecution Evaluation), a benchmark consisting of 800 Python functions (3-13 lines). Each function comes with an input-output…