Systems and methods for using prompt dissection for large language models
Systems and methods for using prompt dissection to prompts one or more machine learning models for a set of one or more documents are disclosed. Exemplary implementations may: store a set of document segments from the set of one or more documents; present a user interface to obtain a compound query that includes a first subquery and a second subquery; create subquery vectors that semantically represent the subqueries; determine a subset of a set of semantic vectors based on comparisons with the subquery vectors; create a combination of the individual document segments that are associated with the subset; provide one or more prompts to the one or more machine learning models, using the combination of the individual document segments as context; present replies from the one or more machine learning models, and/or perform other steps.
1 . A system configured to use prompt dissection to prompt one or more machine learning models for a set of one or more documents, the system comprising:
electronic storage configured to electronically store information, wherein the stored information includes a set of document segments, wherein individual document segments are included in the set of one or more documents, wherein the stored information further includes a set of semantic vectors, wherein individual semantic vectors are associated with individual document segments; and
one or more hardware processors configured by machine-readable instructions to:
effectuate a presentation of a user interface, the user interface being configured to obtain a compound query from a user, wherein the compound query includes two or more subqueries including a first subquery and a second subquery;
create, using the one or more machine learning models, a first subquery vector representing the first subquery;
determine a subset of the set of semantic vectors, wherein the determination is based on comparisons, wherein the comparisons include a first comparison using the first subquery vector;
create a combination of the individual document segments that are associated with the subset of the set of semantic vectors as determined;
provide one or more prompts to the one or more machine learning models that prompt the one or more machine learning models to generate one or more replies, using the created combination of the individual document segments as context for the one or more machine learning models, wherein the one or more prompts are based on the compound query, wherein the second subquery constrains the one or more replies, wherein an individual machine learning model from the one or more machine learning models is limited to a predetermined numbers of tokens as the context for the one or more prompts, and wherein the combination of the individual document segments is created such that the predetermined number of tokens is not exceeded; and
present to the user, through the user interface, the one or more replies obtained from the one or more machine learning models in reply to the one or more prompts, wherein the one or more replies have been constrained by the second subquery.
2 . The system of claim 1 , wherein the first comparison compares similarity between the first subquery vector and individual ones of the set of semantic vectors.
3 . The system of claim 1 , wherein the first comparison compares dissimilarity between the first subquery vector and individual ones of the set of semantic vectors.
4 . The system of claim 1 , wherein the second subquery is a limitation for the one or more replies.
5 . The system of claim 1 , wherein the second subquery is a filter that constrains the one or more replies.
6 . The system of claim 1 , wherein the second subquery is related to formatting of the one or more replies.
7 . The system of claim 1 , wherein the second subquery is related to modifying the one or more replies prior to presentation to the user.
8 . The system of claim 1 , wherein the individual machine learning model is a large language model.
9 . The system of claim 8 , wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3) or a successor of Generative Pre-trained Transformer 3 (GPT3).
10 . A method of using prompt dissection to prompt one or more machine learning models for a set of one or more documents, the method comprising:
electronically storing information, wherein the stored information includes a set of document segments, wherein individual document segments are included in the set of one or more documents, wherein the stored information further includes a set of semantic vectors, wherein individual semantic vectors are associated with individual document segments; and
effectuating a presentation of a user interface that obtains a compound query from a user, wherein the compound query includes two or more subqueries including a first subquery and a second subquery;
creating, using the one or more machine learning models, a first subquery vector representing the first subquery;
determining a subset of the set of semantic vectors, wherein the determination is based on comparisons, wherein the comparisons include a first comparison using the first subquery vector;
creating a combination of the individual document segments that are associated with the subset of the set of semantic vectors as determined;
providing one or more prompts to the one or more machine learning models that prompt the one or more machine learning models to generate one or more replies, using the created combination of the individual document segments as context for the one or more machine learning models, wherein the one or more prompts are based on the compound query, wherein the second subquery constrains the one or more replies, wherein an individual machine learning model from the one or more machine learning models is limited to a predetermined numbers of tokens as the context for the one or more prompts, and wherein the combination of the individual document segments is created such that the predetermined number of tokens is not exceeded; and
presenting to the user, through the user interface, the one or more replies obtained from the one or more machine learning models in reply to the one or more prompts, wherein the one or more replies have been constrained by the second subquery.
11 . The method of claim 10 , wherein the first comparison compares similarity between the first subquery vector and individual ones of the set of semantic vectors.
12 . The method of claim 10 , wherein the first comparison compares dissimilarity between the first subquery vector and individual ones of the set of semantic vectors.
13 . The method of claim 10 , wherein the second subquery is a limitation for the one or more replies.
14 . The method of claim 10 , wherein the second subquery is a filter that constrains the one or more replies.
15 . The method of claim 10 , wherein the second subquery is related to formatting of the one or more replies.
16 . The method of claim 10 , wherein the second subquery is related to modifying the one or more replies prior to presentation to the user.
17 . The method of claim 10 , wherein the individual machine learning model is a large language model.
18 . The method of claim 17 , wherein the large language model is based on or derived from Generative Pre-trained Transformer 3 (GPT3) or a successor of Generative Pre-trained Transformer 3 (GPT3).