Data quality is a key challenge for machine learning and artificial intelligence. There are various ways to solve this problem, but some new and creative approaches include using machine learning to identify and correct errors in data, using neural networks to detect and correct errors automatically, and using large language models to find and correct errors in data.
Traditionally, data quality assurance has been the responsibility of data entry clerks, database administrators, and others who work with data. However, as machine learning and artificial intelligence have become more prevalent, it has become clear that these technologies can be used to automate the process of data quality assurance.
Machine learning can be used to identify errors in data. For example, a machine learning algorithm could be trained on a dataset of known correct data. The algorithm would then identify errors in new data by looking for patterns that do not match the patterns in the correct data.
Neural networks can be used to detect and correct errors in data automatically. For example, a neural network could be trained on a dataset of known correct data. The neural network would then be able to detect and correct errors in new data by looking for patterns that do not match the patterns in the correct data.
Large language models can be used to find and correct errors in data. For example, a large language model could be used to find errors in a dataset of text data. The large language model could find errors in the text data by looking for patterns that do not match the patterns in the correct data.
Using machine learning, neural networks, and large language models to Automate Data Quality Assurance is a new and creative approach that is sure to be effective.
References:
https://arxiv.org/abs/1906.05425