A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

Autor: Bojic, Iva, Halim, Josef, Suharman, Verena, Tar, Sreeja, Ong, Qi Chwen, Phung, Duy, Ravaut, Mathieu, Joty, Shafiq, Car, Josip

Publikováno v: 2023.In The Fourth Workshop on Insights from Negative Results in NLP, pages 19-32, Dubrovnik, Croatia. Association for Computational Linguistics

Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs)

Externí odkaz: http://arxiv.org/abs/2304.00483