A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets
Iva Bojić, Josef Halim, Verena Suharman, Sreeja Tar, Qi Chwen Ong, Duy Phung, Mathieu Ravaut, Shafiq Joty, Josip Car · 2023
Low-quality data can cause downstream problems in high-stakes applications.Data-centric approach emphasizes on improving dataset quality to enhance model performance.Highquality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation.Thus, it is vital to ensure high-quality domain-specific training data.In this paper, we propose a framework for enhancing the data quality of original datasets 1 .We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.