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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
When Models Edit Too Much: On the Fidelity of Minimal Code Edits
TL;DR
Large language models (LLMs) often over-edit code when fixing bugs, leading to unnecessary complexity. A preservation instruction can reduce excessive edits and improve repair accuracy.
✦ Why It Matters
Implement preservation instructions in LLMs to enhance code editing fidelity and reduce unnecessary complexity.
Key Takeaways
- Use preservation instructions to minimize unnecessary code edits.
- Evaluate LLM performance using metrics like Levenshtein distance.
- Consider reinforcement learning for better edit fidelity.
- Be aware of overfitting in supervised fine-tuning for code repairs.
How It Works
The study introduces a preservation instruction that guides LLMs to focus on minimal edits necessary for bug fixes. This instruction helps maintain the original code structure while correcting errors, thereby reducing cognitive load for developers reviewing the changes.