LARGE LANGUAGE MODEL CONTEXT CONCRETIZER

1 . A computer system for determining jailbreak attempts, the computer system comprising:

one or more processors; and

non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to:

receive a query sequence from a client device;

determine a context of the query sequence;

responsive to a determination the context of the query sequence is the associated context:

provide the query sequence to a context concretizer, wherein the context concretizer is configured to process query sequences that include an associated context;

determine, by the context concretizer, whether the query sequence includes a jailbreak attempt for the associated context; and

responsive to a second determination that the query sequence includes the jailbreak attempt, provide an error response to the client device.

2 . The computer system of claim 1 , wherein the instructions further cause the computer system to:

receive the context concretizer;

incorporate the context concretizer into a machine learning model;

receive an alignment reward module; and

incorporate the alignment reward module into the machine learning model.

3 . The computer system of claim 2 , wherein the instructions further cause the computer system to:

receive a reward from the alignment reward module for an identification of the jailbreak attempt, the reward causing the machine learning model to better identify additional jailbreak attempts.

4 . The computer system of claim 1 , wherein the instructions further cause the computer system to:

monitor an alignment of the machine learning model; and

deconstruct layers of the context concretizer to align the machine learning model to prevent hallucinations.

5 . The computer system of claim 4 , wherein the instructions further cause the computer system to:

update the context concretizer to further change the alignment of the machine learning model.

6 . The computer system of claim 1 , wherein the instructions further cause the computer system to:

determine, by a context window, a window of tokens of the query sequence to be processed.

7 . The computer system of claim 6 , wherein the instructions further cause the computer system to:

responsive to the context of the query sequence not being the associated context, provide the query sequence to an attention mechanism.

8 . The computer system of claim 1 , wherein the instructions further cause the computer system to:

responsive to a third determination that the query sequence does not include the jailbreak attempt, provide output that is responsive to the query sequence.

9 . The computer system of claim 1 , wherein an attention manager determines the context using an attention generator and a critic model.

10 . The computer system of claim 1 , wherein the associated context is the financial industry.

11 . A method for determining jailbreak attempts, the method comprising:

receiving a query sequence from a client device;

determining a context of the query sequence;

responsive to a determination the context of the query sequence is the associated context:

providing the query sequence to a context concretizer, wherein the context concretizer is configured to process query sequences that include an associated context;

determining, by the context concretizer, whether the query sequence includes a jailbreak attempt for the associated context; and

responsive to a second determination that the query sequence includes the jailbreak attempt, providing an error response to the client device.

12 . The method of claim 11 , further comprising:

receiving a context concretizer;

incorporating the context concretizer into a machine learning model;

receiving an alignment reward module; and

incorporating the alignment reward module into the machine learning model.

13 . The method of claim 12 , further comprising:

receiving a reward from the alignment reward module for an identification of the jailbreak attempt, the reward causing the machine learning model to better identify additional jailbreak attempts.

14 . The method of claim 11 , further comprising:

monitoring an alignment of the machine learning model; and

deconstructing layers of the context concretizer to align the machine learning model to prevent hallucinations.

15 . The method of claim 14 , further comprising:

updating the context concretizer to further change the alignment of the machine learning model.

16 . The method of claim 11 , further comprising:

determining, by a context window, a window of tokens of the query sequence to be processed.

17 . The method of claim 16 , further comprising:

responsive to the context of the query sequence not being the associated context, providing the query sequence to an attention mechanism.

18 . The method of claim 11 , further comprising:

responsive to a third determination that the query sequence does not include the jailbreak attempt, provide output that is responsive to the query sequence.

19 . The method of claim 11 , the method comprising:

generating the context concretizer for a selected large language model;

generating the alignment award module;

providing the context concretizer and the alignment award module to a large language model device;

monitoring an alignment of the selected large language model; and

deconstruct layers of the context concretizer to align the selected large language model.

20 . The method of claim 19 , further comprising:

generating a second context concretizer for a second large language model;

monitoring a second alignment of the second large language model; and

updating the selected large language model and the second large language model based on new alignment information.