Abstract: An exemplary system for determining whether data sets can be merged together may receive first data labeled with a first variable and second data labeled with a second variable, wherein the first variable represents a first sequence of words and the second variable represents a second sequence of words. One or more machine learning models may generate one or more first candidate words and one or more second candidate words respectively based on the first and second variables. One or more generative machine learning models may generate a predicted first sequence of words and second sequence of words respectively based on the one or more first candidate words and one or more second candidate words. Based on a similarity metric determined based on the predicted sequences and a merging condition, the system may generate a merging instruction indicating whether the first data and the second data are to be merged.

Abstract: An exemplary system for determining whether data sets can be merged together may receive first data labeled with a first variable and second data labeled with a second variable, wherein the first variable represents a first sequence of words and the second variable represents a second sequence of words. One or more machine learning models may generate one or more first candidate words and one or more second candidate words respectively based on the first and second variables. One or more generative machine learning models may generate a predicted first sequence of words and second sequence of words respectively based on the one or more first candidate words and one or more second candidate words. Based on a similarity metric determined based on the predicted sequences and a merging condition, the system may generate a merging instruction indicating whether the first data and the second data are to be merged.