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Researchers from Google DeepMind, in collaboration with Yale University, have developed a new self-training machine learning method named Naturalized Execution Tuning (NExT). This innovative approach significantly enhances the ability of large language models (LLMs) to understand and reason about code execution. The method addresses a critical issue where LLMs, traditionally trained on textual representations of programs, lack a deep semantic understanding of program execution during runtime.
💙DeepMind Researchers Propose Naturalized Execution Tuning (NExT): A Self-Training Machine Learning Method that Drastically Improves the LLM's Ability to Reason about Code Execution https://t.co/KxTc75vm3c
DeepMind Researchers Propose Naturalized Execution Tuning (NExT): A Self-Training Machine Learning Method that Drastically Improves the LLM’s Ability to Reason about Code Execution https://t.co/BOiTQW3hIg
"NExT: Teaching Large Language Models to Reason about Code Execution" The key problem this paper aims to solve is that, (LLMs) of code are typically trained on the surface textual form of programs, thus may lack a semantic understanding of how programs execute at run-time. So… https://t.co/n3dfajFHV0
