AIBullisharXiv – CS AI · Apr 106/10
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Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
Researchers propose fine-grained confidence calibration methods for large language models in automated code revision tasks, addressing the limitation of traditional global calibration approaches. By applying local Platt-scaling to task-specific confidence scores, the study demonstrates improved calibration accuracy across multiple code repair and refinement tasks, enabling developers to better trust LLM outputs.