no code implementations • 29 Feb 2024 • Alex Gu, Wen-Ding Li, Naman jain, Theo X. Olausson, Celine Lee, Koushik Sen, Armando Solar-Lezama
In this work, we focus on these counterfeit samples: programs sampled from a language model that 1) have a high enough log-probability to be generated at a moderate temperature and 2) pass weak correctness checks.
no code implementations • 25 Sep 2023 • Celine Lee, Abdulrahman Mahmoud, Michal Kurek, Simone Campanoni, David Brooks, Stephen Chong, Gu-Yeon Wei, Alexander M. Rush
In this work, we leverage the strengths of LMs and symbolic solvers in a neurosymbolic approach to learned transpilation for assembly code.
1 code implementation • 2 Mar 2023 • Derek Chen, Celine Lee, Yunan Lu, Domenic Rosati, Zhou Yu
Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns.
no code implementations • 26 Apr 2021 • Celine Lee, Justin Gottschlich, Dan Roth
With the growth of natural language processing techniques and demand for improved software engineering efficiency, there is an emerging interest in translating intention from human languages to programming languages.