PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
Deepcode ai fix: Fixing security vulnerabilities with large language models
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LiL vulnerabilities are more severe than ecosystem and conventional bugs and drop LLM-based repair Pass@1 by ~10.8%, with three categories often at 0% success.
Local Platt scaling on three fine-grained confidence scores reduces calibration error for LLM-based automated code revision across tasks and models compared to global scaling alone.
citing papers explorer
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PerfCoder: Large Language Models for Interpretable Code Performance Optimization
PerfCoder is a family of LLMs trained on optimization trajectories with human annotations and runtime-based preference alignment that achieves higher runtime speedups and optimization rates on the PIE benchmark than prior models while producing interpretable feedback.
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Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities
LiL vulnerabilities are more severe than ecosystem and conventional bugs and drop LLM-based repair Pass@1 by ~10.8%, with three categories often at 0% success.
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Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revision
Local Platt scaling on three fine-grained confidence scores reduces calibration error for LLM-based automated code revision across tasks and models compared to global scaling alone.