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Regrading model outputs can shift correctness labels by 9% and double the performance spread, revealing critical flaws in conventional evaluation methods for LLM code generation.
Epic-organized LLM-generated Gherkin scenarios are rated significantly higher in quality than those generated by a naive baseline, despite similar semantic coverage.
