Localized Uncertainty for Code LLMs
Analysis
This paper addresses the critical issue of LLM output reliability in code generation. By providing methods to identify potentially problematic code segments, it directly supports the practical use of LLMs in software development. The focus on calibrated uncertainty is crucial for enabling developers to trust and effectively edit LLM-generated code. The comparison of white-box and black-box approaches offers valuable insights into different strategies for achieving this goal. The paper's contribution lies in its practical approach to improving the usability and trustworthiness of LLMs for code generation, which is a significant step towards more reliable AI-assisted software development.
Key Takeaways
- •Proposes techniques to localize potentially misaligned code generated by LLMs.
- •Introduces a dataset of "Minimal Intent Aligning Patches" for evaluation.
- •Compares white-box and black-box approaches for uncertainty calibration.
- •Demonstrates that a small supervisor model can effectively estimate edited lines.
- •Discusses generalizability and connections to AI oversight and control.
“Probes with a small supervisor model can achieve low calibration error and Brier Skill Score of approx 0.2 estimating edited lines on code generated by models many orders of magnitude larger.”