An iterative two-loop LLM pipeline (a faithfulness loop with execution-based checks and a personalization loop with a role-playing judge) produces code explanations that score higher on automatic metrics than simpler baselines.
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From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models
An iterative two-loop LLM pipeline (a faithfulness loop with execution-based checks and a personalization loop with a role-playing judge) produces code explanations that score higher on automatic metrics than simpler baselines.