Systems and method for large language model-based differentiation

1 . A system for Large Language Model (LLM) based differentiation, the system comprising:

a processor; and

a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:

receive input data associated with an entity;

classify the input data to a descriptive class;

command an adaptive web crawler to retrieve descriptive content associated with the descriptive class;

extract a plurality of positioning signals from the input data associated with the entity;

generate a contrast score for each positioning signal of the plurality of positioning signals by comparing the plurality of positioning signals to the descriptive content;

encode the contrast score into a differentiator profile comprising at least one categorical tag and at least one weighted relationship for each positioning signal;

modify a generation behavior of a base LLM using the differentiator profile as a conditioning input; and

generate, by the base LLM conditioned on the differentiator profile, one or more differentiator outputs.

2 . The system of claim 1 , wherein:

commanding the adaptive web crawler to retrieve the descriptive content comprises:

transforming the descriptive content into a plurality of descriptive embeddings within a shared feature space; and

extracting the plurality of positioning signals from the input data associated with the entity comprises:

transforming the input data into a plurality of signal embeddings representing the entity in the shared feature space such that each positioning signal comprises a signal embedding within the shared feature space.

3 . The system of claim 2 , wherein generating the contrast score for each positioning signal comprises:

identifying a distance between each signal embedding and each descriptive embedding; and

generating the contrast score based on the distance.

4 . The system of claim 1 , wherein:

classifying the input data to the descriptive class comprises:

applying a multi-label classifier configured to output a confidence vector for each descriptive class of a plurality of descriptive classes; and

commanding the adaptive web crawler to retrieve the descriptive content comprises triggering one or more class-specific crawl policies for the adaptive web crawler based on the descriptive content.

5 . The system of claim 1 , wherein the processor is further configured to:

generate a resonance score indicative of a predicted response for each differentiator output of the one or more differentiator outputs; and

display the one or more differentiator outputs and the resonance score through a graphical user interface.

6 . The system of claim 5 , wherein generating the resonance score comprises generating the resonance score using a resonance machine learning model trained with training data comprising a plurality of historical differentiator outputs correlated to a plurality of historical responses.

7 . The system of claim 1 , wherein the input data comprises a meeting transcript.

8 . The system of claim 1 , wherein generating the one or more differentiator outputs comprises:

transmitting, to the base LLM, an entity-specific request; and

receiving, from the base LLM, the one or more differentiator outputs.

9 . The system of claim 1 , wherein encoding the contrast score into the differentiator profile comprises:

receiving an existing differentiator profile associated with the entity; and

updating one or more categorical tags and one or more weighted relationships within the existing differentiator profile for each positioning signal.

10 . The system of claim 1 , wherein generating the contrast score for each positioning signal comprises:

computing a benefit score for each positioning signal based on the comparison between each positioning signal and the descriptive content; and

filtering out contrast scores associated with a negative benefit score.

11 . A method for Large Language Model (LLM) based differentiation, the method comprising:

receiving, by at least a processor, input data associated with an entity;

classifying, by the at least a processor, the input data to a descriptive class;

commanding, by the at least a processor, an adaptive web crawler to retrieve descriptive content associated with the descriptive class;

extracting, by the at least a processor, a plurality of positioning signals from the input data associated with the entity;

generating, by the at least a processor, a contrast score for each positioning signal of the plurality of positioning signals by comparing the plurality of positioning signals to the descriptive content;

encoding, by the at least a processor, the contrast score into a differentiator profile comprising at least one categorical tag and at least one weighted relationship for each positioning signal;

modifying, by the at least a processor, a generation behavior of a base LLM using the differentiator profile as a conditioning input; and

generating, by the base LLM conditioned on the differentiator profile, one or more differentiator outputs.

12 . The method of claim 11 , wherein:

commanding the adaptive web crawler to retrieve the descriptive content comprises:

transforming the descriptive content into a plurality of descriptive embeddings within a shared feature space; and

extracting the plurality of positioning signals from the input data associated with the entity comprises:

transforming the input data into a plurality of signal embeddings representing the entity in the shared feature space such that each positioning signal comprises a signal embedding within the shared feature space.

13 . The method of claim 12 , wherein generating the contrast score for each positioning signal comprises:

identifying a distance between each signal embedding and each descriptive embedding; and

generating the contrast score based on the distance.

14 . The method of claim 11 , wherein:

classifying the input data to the descriptive class comprises:

applying a multi-label classifier configured to output a confidence vector for each descriptive class of a plurality of descriptive classes; and

commanding the adaptive web crawler to retrieve the descriptive content comprises triggering one or more class-specific crawl policies for the adaptive web crawler based on the descriptive content.

15 . The method of claim 11 , the method further comprising:

generating, by the at least a processor, a resonance score indicative of a predicted response for each differentiator output of the one or more differentiator outputs; and

displaying, by the at least a processor, the one or more differentiator outputs and the resonance score through a graphical user interface.

16 . The method of claim 15 , wherein generating the resonance score comprises generating the resonance score using a resonance machine learning model trained with training data comprising a plurality of historical differentiator outputs correlated to a plurality of historical responses.

17 . The method of claim 11 , wherein the input data comprises a meeting transcript.

18 . The method of claim 11 , wherein generating the one or more differentiator outputs comprises:

transmitting, to the base LLM, an entity-specific request; and

receiving, from the base LLM, the one or more differentiator outputs.

19 . The method of claim 11 , wherein encoding the contrast score into the differentiator profile comprises:

receiving an existing differentiator profile associated with the entity; and

updating one or more categorical tags and one or more weighted relationships within the existing differentiator profile for each positioning signal.

20 . The method of claim 11 , wherein generating the contrast score for each positioning signal comprises:

computing a benefit score for each positioning signal based on the comparison between each positioning signal and the descriptive content; and

filtering out contrast scores associated with a negative benefit score.