Flexible entity resolution networks

A method comprising:

• receiving a plurality of different machine learning models;
• testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;
• selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;
• receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;
• identifying a plurality of first attributes in a data model;
• identifying a plurality of second attributes in the data model;
• creating a first string based on the plurality of first attributes in the data model;
• creating a second string based on the plurality of second attributes in the data model;
• generating a first prompt based on the first string;
• generating a second prompt based on the second string;
• providing the first prompt to the domain-agnostic large language model;
• generating, by the domain-agnostic large language model using the first prompt, a first domain-agnostic large language model result;
• clipping the first domain-agnostic large language model result;
• providing the second prompt to the domain-agnostic large language model;
• generating, by the domain-agnostic large language model using the second prompt, a second domain-agnostic large language model result;
• clipping the second domain-agnostic large language model result;
• generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second domain-agnostic large language model results;
• merging, based on the machine learning final result, the first entity and the second entity.

A system comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the system to perform:
• receiving a plurality of different machine learning models;
• testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;
• selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;
• swapping the domain-agnostic large language model with an approximator network, wherein the approximator network approximates outputs of the domain-agnostic large language model;
• receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;
• identifying a plurality of first attributes in a data model;
• identifying a plurality of second attributes in the data model;
• creating a first string based on the plurality of first attributes in the data model;
• creating a second string based on the plurality of second attributes in the data model;
• generating a first prompt based on the first string;
• generating a second prompt based on the second string;
• providing the first prompt to the approximator network;
• generating, by the approximator network using the first prompt, a first approximator network result;
• clipping the first approximator network result;
• providing the second prompt to another approximator network;
• generating, by the other approximator network using the second prompt, a second approximator network result;
• clipping the second approximator network result;
• generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second approximator network results;
• merging, based on the machine learning final result, the first entity and the second entity.

A method comprising:

• receiving a plurality of different machine learning models;
• testing each of the plurality of different machine learning models, the testing including simulating a respective match performance of each of the plurality of different machine learning models;
• selecting, based on the testing and the simulating, a domain-agnostic large language model of the plurality of different machine learning models;
• swapping the domain-agnostic large language model with an approximator network, wherein the approximator network approximates outputs of the domain-agnostic large language model;
• receiving an entity resolution request, the entity resolution request indicating a first entity and a second entity;
• identifying a plurality of first attributes in a data model;
• identifying a plurality of second attributes in the data model;
• creating a first string based on the plurality of first attributes in the data model;
• creating a second string based on the plurality of second attributes in the data model;
• generating a first prompt based on the first string;
• generating a second prompt based on the second string;
• providing the first prompt to the approximator network;
• generating, by the approximator network using the first prompt, a first approximator network result;
• clipping the first approximator network result;
• providing the second prompt to another approximator network;
• generating, by the other approximator network using the second prompt, a second approximator network result;
• clipping the second approximator network result;
• generating, by a downstream neural network classifier, a machine learning final result based on the clipped first and second approximator network results;
• merging, based on the machine learning final result, the first entity and the second entity.