The paper delivers a taxonomy of seven LLM study types in software engineering along with eight guidelines that separate mandatory requirements from recommended practices to address reproducibility challenges.
arXiv preprint arXiv:2304.11085 (2023)
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Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.
citing papers explorer
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A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Relabeler is an end-to-end framework that detects corrupted labels via local and global instance relationships and corrects them using feature-based estimation, reporting up to 58% better label correction precision than baselines.
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Noise-Aware Framework for Correcting Corrupted Labels
CANOLA estimates label noise and performs cautious iterative soft-label refinement to correct corrupted training data, reporting 19-52% error reduction versus prior methods on six datasets.