Summary of Don’t Transform the Code, Code the Transforms: Towards Precise Code Rewriting Using Llms, by Chris Cummins et al.
Don’t Transform the Code, Code the Transforms: Towards Precise Code Rewriting using LLMs
by Chris Cummins, Volker Seeker, Jordi Armengol-Estapé, Aram H. Markosyan, Gabriel Synnaeve, Hugh Leather
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes leveraging large language models (LLMs) for improving the efficiency and accuracy of code rewriting, refactoring, and optimization tools. Despite being inherently slow and incorrect, LLMs can be harnessed to enhance the quality of these tools. The study aims to explore this opportunity in optimizing code. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Code rewriting and optimization are crucial tasks in software development, but current tools often fall short due to their inefficiency or inaccuracy. This paper explores the potential of large language models (LLMs) in improving these tools by leveraging their capabilities for text processing and manipulation. |
Keywords
* Artificial intelligence * Optimization
