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

• URL: http://arxiv.org/abs/2304.00483v2
• Date: Fri, 26 May 2023 05:43:19 GMT
• Title: A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets
• Authors: Iva Bojic, Josef Halim, Verena Suharman, Sreeja Tar, Qi Chwen Ong, Duy Phung, Mathieu Ravaut, Shafiq Joty, Josip Car
• Abstract summary: Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance.
• Score: 5.673449249014538
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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