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Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation
April 16, 2024, 4:51 a.m. | Juhwan Choi, Jungmin Yun, Kyohoon Jin, YoungBin Kim
cs.CL updates on arXiv.org arxiv.org
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 …
abstract annotation arxiv construction cost cs.ai cs.cl data data annotation dataset datasets hiring however human issue llm performance process quality reliability through type via
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