Intuit, Inc.

Hadas Baumer

Yair Horesh

Itay Margolin

Sigalit Bechler

Natalie Bar Eliyahu

Omer Wosner

Lior Tabori

Linoy Cohen

Shon Mendelson

Tom Klein

A method including receiving a command to perform a one-to-many matching task between first and second datasets. The first and second datasets are vectorized into first and second embedded datasets. A first self-attention model is executed on the first embedded dataset to generate a first attention dataset in which each value of a first number of first features of the first dataset is weighted based on each other value of the first number of first features. A second self-attention model is executed on the second embedded dataset to generate a second attention dataset in which each value of a second number of second features of the second dataset is weighted based on each other value of the second number of second features. The first and second attention datasets are combined into a relationship matrix expressing relationships between the first and second features. The method also includes returning the relationship matrix.

System and method for one-to-many matching between disparate datasets

A system for vendor deduplication. The system creates embeddings of entity names and creates an initial entity graph comprising entities whose embeddings are related. The initial entity graph includes nodes representing the entity names linked together by edge weights indicating their similarity. The system adjusts the edge weights between the entity names according to transactional data related to the entities to create a final entity graph and merges the entity names as a common entity based on the adjusted edge weights in the final entity graph.

Two-fold vendor de-duplication via node embeddings

A method of executing a matching language model to perform a many-to-one matching task. The matching task uses a number of tokens that exceeds a token limit of the matching language model. The number of tokens is reduced to a reduced number of tokens by executing a rule-based application on a target entry and a dataset of entries to output a data subset including fewer entries. The reduced number of tokens are reduced within the token limit by executing a sorting language model on the data subset and the target entry to output candidate matching sets. The candidate matching sets are evaluated as to whether a potential match exists with the target entry. The matching language model is executed on the number of candidate matching sets and the target entry to output a selected matching set matching the target entry. The selected matching set is returned.

Large language model ensemble for combinatorial retrieval in one-to-many matching tasks

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Petah Tikva, Israel

Hadas Baumer