Baran: Effective Error Correction via a Unified Context Representation and Transfer Learning
Summary: Baran introduces a unified context representation and transfer learning to fuse multiple error-corrector models for data repair. Modeling full context—value, tuple co-occurrences, and attribute type—yields richer candidates and higher precision, with Wikipedia pretraining boosting recall and needing ~20 labeled tuples. (summarized by gpt-5-nano on Feb 09 2026)
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