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arXiv · 2304.00483

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

Iva Bojic·Josef Halim·Verena Suharman·Sreeja Tar·Qi Chwen Ong·Duy Phung·Mathieu Ravaut·Shafiq Joty·Josip Car
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Abstract

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) 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. 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.

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Iva Bojic, Josef Halim, Verena Suharman, Sreeja Tar, Qi Chwen Ong, Duy Phung, Mathieu Ravaut, Shafiq Joty, Josip Car. 2023-04-02. A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets. https://doi.org/10.18653/v1%2F2023.insights-1.3

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