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.

本文提出了一个旨在增强原始数据集数据质量的框架，并应用于四个生物医学数据集，使用回译技术提高数据集质量，在 BioASQ 数据集上表现出相对提高了 33%/40% 的检索 / 阅读器模型的微调效果。