Systems and method for large language model-based differentiation
A system for Large Language Model (LLM) based differentiation, the system including a processor configured 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 including 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.
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.