Aktuelles: Publication at QDB@VLDB 2026

Our paper "Can LLMs Serve as a Data Error Detection Engine? Trade-offs in Accuracy, Cost, and Hallucination Across Datasets" (Maximilian Plazotta and Meike Klettke) was featured at the 15th International Workshop on Quality in Databases (QDB'26), held at the 52nd VLDB conference in Boston, MA, USA. It investigates the trade-offs of using large language models as a data error detection engine, testing for accuracy, computational cost, and risk of hallucination across different datasets.