Multidimensional analysis of communication records using LLMS

A system comprising:

• a non-transitory computer-readable medium;
• and
• one or more processors communicatively connected to the non-transitory computer-readable medium, the one or more processors configured to execute processor-executable instructions stored in the non-transitory computer-readable medium to cause the one or more processors to:
• receive a set of communication records, the set of communication records representing one or more communications between a first person and a second person;
• receive a user query, the user query comprising one or more constraints;
• generate a plurality of segments from the communication records, at least a subset of the plurality of segments based on the one or more constraints;
• generate, using an autoencoder, segment embeddings for each segment of the plurality of segments and constraint embeddings for each constraint of the one or more constraints;
• and
• select one or more segments based on the one or more constraints based on determined similarities between segment embeddings and constraint embeddings;
• generate one or more queries based on the one or more constraints;
• provide, to a trained large language model (“LLM”), the one or more selected segments and the one or more generated queries;
• and
• receive, from the trained LLM, and outputting a multidimensional analysis of the set of communication records.

A non-transitory computer-readable medium comprising processor-executable instructions configured to cause one or more processors to:

• receive a set of communication records, the set of communication records representing one or more communications between a first person and a second person;
• receive a user query, the user query comprising one or more constraints;
• generate a plurality of segments from the communication records, at least a subset of the plurality of segments based on the one or more constraints;
• generate, using an autoencoder, segment embeddings for each segment of the plurality of segments and constraint embeddings for each constraint of the one or more constraints;
• and
• select one or more segments based on the one or more constraints based on determined similarities between segment embeddings and constraint embeddings;
• generate one or more queries based on the one or more constraints;
• provide, to a trained large language model (“LLM”), the one or more selected segments and the one or more generated queries;
• and
• receive, from the trained LLM, and outputting a multidimensional analysis of the set of communication records.