Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation

arXiv:2404.09682v1 Announce Type: new
Abstract: The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been made to correct this issue through human annotators. However, hiring and managing human annotators is expensive and time-consuming. As an alternative, recent studies are exploring the use of large language models (LLMs) for data annotation.
In this study, we present a case …

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