Resolving complex match candidates using large language model explanations of ambiguous features

Abstract

A method includes obtaining, from a matching model, for a user input, a set of candidate matches generated using a corresponding set of features. The corresponding set of features is partitioned into a first feature subset and a second feature subset. For each candidate match in the set of candidate matches, a candidate feature subset of features is individually generated. The candidate feature subset of features are selected from the first feature subset and the second feature subset for the candidate match. A multitude of candidate feature subsets corresponding to the set of candidate matches is obtained. The multitude of candidate feature subsets is clustered to obtain a first feature pattern and a second feature pattern. A large language model (LLM) generates a suggestion to modify the user input by processing the first feature pattern and the second feature pattern. The method further includes presenting the suggestion of the LLM.