LARGE LANGUAGE MODEL BASED NESTED SUMMARIZATION OF CONVERSATION DATA

1 . A computer-implemented method comprising:

receiving, at a server system, a free-form query;

selecting two-way communication history records from a database associated with the free-form query to identify selected history records; and

generating individual summaries of individual history records of the selected history records by:

processing a set of corresponding individual history records using a chunking algorithm;

constructing language responses from outputs of the chunking algorithm using a large language model;

aggregating the language responses; and

processing the language responses using the large language model to generate an individual summary for corresponding individual history records.

2 . The computer-implemented method of claim 1 , wherein the selected history records are selected from the database either randomly or using a search algorithm.

3 . The computer-implemented method of claim 1 , wherein a number of two-way communication history records is less than or equal to 50.

4 . The computer-implemented method of claim 1 , further comprising:

selecting a second number of two-way communication history records from a database associated with the free-form query to identify second selected history records;

generating second individual summaries of individual history records of the second selected history records by:

processing a second set of corresponding individual history records using the chunking algorithm;

constructing second language responses from second outputs of the chunking algorithm using the large language model;

aggregating the second language responses; and

processing the second language responses using the large language model to generate a second individual summary for second corresponding individual history records; and

generating, using the large language model, an aggregate summary using the individual summary and the second individual summary.

5 . The computer-implemented method of claim 1 , further comprising:

generating a plurality of individual summaries for sets of communication history records numbering less than or equal to a threshold number;

processing the plurality of individual summaries using a second large language model different than the large language model to generate an aggregated summary for data of the sets of communication history records.

6 . The computer-implemented method of claim 1 , further comprising:

generating a summary query and an aggregation query from the free-form query; generating a plurality of individual summaries for sets of communication history records including the individual summaries using the summary query; and

processing the plurality of individual summaries using the aggregation query and a second large language model.

7 . The computer-implemented method of claim 1 , further comprising:

determining whether a number of two-way communication history records is greater than a threshold number; and

facilitating presentation of the individual summary for the corresponding individual history records as a response to the free-form query when the number of two-way communication history records is not greater than the threshold number.

8 . The computer-implemented method of claim 1 , further comprising:

determining whether a number of two-way communication history records is greater than a threshold number, wherein when the number is greater than the threshold number, a response to the free-form query is generated by:

dividing the number of two-way communication history records into sets of records including fewer than the threshold number of records;

generating, by the large language model, individual summaries for the sets of records; and

generating, by the large language model, the response as an aggregated summary using the individual summaries.

9 . A system comprising:

a memory; and

one or more processors coupled to the memory and configured to perform operations comprising:

receiving, at a server system, a free-form query;

selecting two-way communication history records from a database associated with the free-form query to identify selected history records; and

generating individual summaries of individual history records of the selected history records by:

processing a set of corresponding individual history records using a chunking algorithm;

constructing language responses from outputs of the chunking algorithm using a large language model;

aggregating the language responses; and

processing the language responses using the large language model to generate an individual summary for corresponding individual history records.

10 . The system of claim 9 , wherein the selected history records are selected from the database either randomly or using a search algorithm.

11 . The system of claim 9 , wherein a number of two-way communication history records is less than or equal to 50.

12 . The system of claim 9 , wherein the one or more processors are further configured to perform operations comprising:

selecting a second number of two-way communication history records from a database associated with the free-form query to identify second selected history records;

generating second individual summaries of individual history records of the second selected history records by:

processing a second set of corresponding individual history records using the chunking algorithm;

constructing second language responses from second outputs of the chunking algorithm using the large language model;

aggregating the second language responses; and

processing the second language responses using the large language model to generate a second individual summary for second corresponding individual history records; and

generating, using the large language model, an aggregate summary using the individual summary and the second individual summary.

13 . The system of claim 9 , wherein the one or more processors are further configured to perform operations comprising:

generating a plurality of individual summaries for sets of communication history records numbering less than or equal to a threshold number;

processing the plurality of individual summaries using a second large language model different than the large language model to generate an aggregated summary for data of the sets of communication history records.

14 . The system of claim 9 , wherein the one or more processors are further configured to perform operations comprising:

generating a plurality of individual summaries for sets of communication history records numbering less than or equal to a threshold number;

processing the plurality of individual summaries using a second large language model different than the large language model to generate an aggregated summary for data of the sets of communication history records.

15 . The system of claim 9 , wherein the one or more processors are further configured to perform operations comprising:

generating a summary query and an aggregation query from the free-form query; generating a plurality of individual summaries for sets of communication history records including the individual summaries using the summary query; and

processing the plurality of individual summaries using the aggregation query and a second large language model.

16 . A non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising:

receiving, at a server system, a free-form query;

selecting two-way communication history records from a database associated with the free-form query to identify selected history records; and

generating individual summaries of individual history records of the selected history records by:

processing a set of corresponding individual history records using a chunking algorithm;

constructing language responses from outputs of the chunking algorithm using a large language model;

aggregating the language responses; and

processing the language responses using the large language model to generate an individual summary for corresponding individual history records.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein the selected history records are selected from the database either randomly or using a search algorithm.

18 . The non-transitory computer-readable storage medium of claim 16 , wherein a number of two-way communication history records is less than or equal to 50.

19 . The non-transitory computer-readable storage medium of claim 16 , wherein when executed by one or more processors of a computing system, the instructions cause the computing system to perform operations comprising:

generating a plurality of individual summaries for sets of communication history records numbering less than or equal to a threshold number;

processing the plurality of individual summaries using a second large language model different than the large language model to generate an aggregated summary for data of the sets of communication history records.

20 . The non-transitory computer-readable storage medium of claim 16 , wherein when executed by one or more processors of a computing system, the instructions cause the computing system to perform operations comprising:

generating a summary query and an aggregation query from the free-form query; generating a plurality of individual summaries for sets of communication history records including the individual summaries using the summary query; and

processing the plurality of individual summaries using the aggregation query and a second large language model.