Goldman Sachs &co. LLC

Konstantin Kuchenmeister

Bing Xiang

Demetrius Rowland

Alysa V Shcherbakova

The described system provides a dual-model framework for data retrieval from complex data environments such as webpages on the internet. It combines a traditional similarity model that identifies relevant data from vast amounts of data and a large language model that delves deeper into the relevant data to uncover specifics. The models, in conjunction, provide a method for providing responses to structured queries about an entity. A source investigator receives a request for information about an entity alongside a set of keywords. A source datastore is identified for the entity and a similarity model is applied to the datastore to determine relevancy scores for data within. Data and/or nodes above a relevancy threshold are stored as relevant data. Then, using the large language model, the investigator generates responses to the structured queries based on the relevant data and provides responses to the user system.

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New York, NY, United States of America

Konstantin Kuchenmeister