Systems and methods for implementing permission bypass for large language models

1 . A system, the system comprising:

one or more electronic processors configured to:

receive, from a user device of a user, a user query pertaining to a topic;

determine, responsive to a semantic search of a vector database storing a plurality of vector embeddings based on a user query embedding for the user query, a plurality of electronic files related to the topic of the user query;

determine, based on a permission level of the user, an accessibility of a first portion of the plurality of electronic files, wherein the first portion of the plurality of electronic files are accessible to the user under the permission level;

determine, for the permission level, a first set of vector embeddings of the plurality of vector embeddings, the first set of vector embeddings representing the first portion of the plurality of electronic files being accessible under the permission level;

determine a second set of vector embeddings of the plurality of vector embeddings, the second set of vector embeddings representing the plurality of electronic files, wherein the second set of vector embeddings includes the first set of vector embeddings and a third set of vector embeddings representing a second portion of the plurality of electronic files being inaccessible to the user under the permission level;

determine a first similarity metric between the first set of vector embeddings and the user query embedding;

determine a second similarity metric between the second set of vector embeddings and the user query embedding;

determine a difference between the first similarity metric and the second similarity metric; and

generate, using a large language model (“LLM”), a response to the user query based on the difference and the first portion of the plurality of electronic files.

2 . The system of claim 1 , wherein the one or more electronic processors are configured to:

generate, using an embedding model, the user query embedding for the user query; and

execute, based on the user query embedding, the semantic search of the vector database.

3 . The system of claim 1 , wherein the one or more electronic processors are configured to determine the first portion of the plurality of electronic files by matching the user query embedding to a set of vector embeddings of the plurality of vector embeddings, wherein the set of vector embeddings represent the first portion of the plurality of electronic files.

4 . The system of claim 1 , wherein the one or more electronic processors are configured to:

determine that the first difference is within a first threshold; and

execute a first LLM query using the first portion of the plurality of electronic files,

wherein the response to the user query is generated based on the first LLM query executed using the first portion of the plurality of electronic files.

5 . The system of claim 1 , wherein the one or more electronic processors are configured to:

determine that the difference exceeds a first threshold;

execute a second LLM query using the first portion of the plurality of electronic files;

execute a third LLM query using the plurality of electronic files;

generate a second LLM query response embedding representing a first output of the second LLM query response;

generate a third LLM query response embedding representing a second output of the third LLM query response; and

determine a third similarity metric between the second LLM query response embedding and the third LLM query response embedding,

wherein the response to the user query is generated based on the third similarity metric.

6 . The system of claim 5 , wherein the one or more electronic processors are configured to:

determine that the third similarity metric satisfies a second threshold; and

execute, using the LLM, a fourth LLM query using the first portion of the plurality of electronic files;

wherein the response to the user query is generated based on execution of the fourth LLM query.

7 . The system of claim 6 , wherein the one or more electronic processors are configured to:

generate a notification to indicate that the permission level impacted the response.

8 . The system of claim 6 , wherein the one or more electronic processors are configured to:

generate a set of instructions for changing the permission level to a different permission level.

9 . The system of claim 1 , wherein the one or more electronic processors are configured to:

train, with training data, the LLM model using machine learning, wherein the LLM model is an artificial neural network.

10 . The system of claim 1 , wherein the LLM model is trained using at least one of self-supervised learning or semi-supervised learning.

11 . A method, the method comprising:

receiving, with one or more electronic processors, a user query from a user device of a user, the user query being related to a topic;

executing, with the one or more electronic processors, a search of a vector database based on the user query;

determining, with the one or more electronic processors, based on the search, electronic content related to the topic of the user query;

determining, with the one or more electronic processors, based on a permission level, an accessibility of the electronic content to the user, wherein a first portion of the electronic content is accessible to the user under the permission level and a second portion of the electronic content is inaccessible to the user under the permission level;

executing, with the one or more electronic processors, using a large language model “(LLM”), a first LLM query using the first portion of the electronic content;

executing, with the one or more electronic processors, using the LLM, a second LLM query using the first portion and the second portion of the electronic content; and

generating, with the one or more electronic processors, a first response to the user query based on a similarity of a first output of the first LLM query and a second output of the second LLM query.

12 . The method of claim 11 , further comprising:

determining that the first portion of the electronic content is accessible to the user under the permission level; and

determining that the second portion of the electronic content is inaccessible to the user under the permission level.

13 . The method of claim 11 , further comprising:

determining that the first portion of the electronic content is accessible to the user under the permission level;

determining that the second portion of the electronic content is inaccessible to the user under the permission level;

generating, using an embedding model, a first LLM query embedding for a first output of the first LLM query;

generating, using the embedding model, a second LLM query embedding for a second output of the second LLM query;

determining a similarity metric between the first LLM query embedding and the second LLM query embedding; and

generating, based on the similarity metric, a fourth response to the user query.

14 . The method of claim 11 , further comprising:

generating, using an embedding model, a user query embedding for the user query; and

identifying a plurality of vector embeddings from the vector database, the plurality of vector embeddings being within a similarity threshold of the user query embedding,

wherein the plurality of vector embeddings represent the electronic content.

15 . A non-transitory, computer-readable medium storing instructions that, when executed by an electronic processor, perform a set of functions, the set of functions comprising:

receiving, from a user device of a user, a user query;

generating, using an embedding model, a user query embedding for the user query;

executing a semantic search of a vector database to identify, based on the user query embedding, a plurality of vector embeddings from the vector database;

determining, based on the plurality of vector embeddings, a plurality of electronic files related to the user query;

determining, based on a permission level, an accessibility of the plurality of electronic files for the user;

determining, for the permission level, a first set of vector embeddings of the plurality of vector embeddings, the first set of vector embeddings representing a first portion of the plurality of electronic files being accessible under the permission level;

determining a second set of vector embeddings of the plurality of vector embeddings, the second set of vector embeddings representing the plurality of electronic files, wherein the second set of vector embeddings includes the first set of vector embeddings and a third set of vector embeddings representing a second portion of the plurality of electronic files being inaccessible to the user under the permission level;

determine a difference between a first similarity metric and a second similarity metric, wherein the first similarity metric is between the first set of vector embeddings and the user query embedding and the second similarity metric is between the second set of vector embeddings and the user query embedding; and

generate, using a large language model (“LLM”), a response to the user query based on the difference.

16 . The computer-readable medium of claim 15 , wherein the set of functions further comprises:

determining an impact of the permission level on responding to the user query,

wherein generating the response to the user query includes generating the response to the user query based on the impact.

17 . The computer-readable medium of claim 16 , wherein generating the response to the user query based on the impact includes at least one of:

generating the response to the user query based on the first portion of the electronic files; or

generating a notification indicating the impact of the permission level on the response.