Abstract: At least one processor can receive a text input from a user interface (UI) comprising at least a portion of a prompt to a large language model (LLM). The at least one processor can classify the text input as having a negative sentiment classification using a machine learning (ML) model configured to classify inputs according to expected user sentiments in reaction to an LLM response, the ML model being configured to classify the inputs from available classifications including at least a positive sentiment classification and one or more available negative sentiment classifications. In response to the classifying, the at least one processor can prevent input of the prompt to the LLM, determine information to add to the prompt to change the negative sentiment classification to the positive sentiment classification, and cause the UI to display a reply requesting the information to add to the prompt.

Abstract: At least one processor can receive a text input from a user interface (UI) comprising at least a portion of a prompt to a large language model (LLM). The at least one processor can classify the text input as having a negative sentiment classification using a machine learning (ML) model configured to classify inputs according to expected user sentiments in reaction to an LLM response, the ML model being configured to classify the inputs from available classifications including at least a positive sentiment classification and one or more available negative sentiment classifications. In response to the classifying, the at least one processor can prevent input of the prompt to the LLM, determine information to add to the prompt to change the negative sentiment classification to the positive sentiment classification, and cause the UI to display a reply requesting the information to add to the prompt.

Abstract: At least one processor can receive a text input from a user interface (UI) comprising at least a portion of a prompt to a large language model (LLM). The at least one processor can classify the text input as having a negative sentiment classification using a machine learning (ML) model configured to classify inputs according to expected user sentiments in reaction to an LLM response, the ML model being configured to classify the inputs from available classifications including at least a positive sentiment classification and one or more available negative sentiment classifications. In response to the classifying, the at least one processor can prevent input of the prompt to the LLM, determine information to add to the prompt to change the negative sentiment classification to the positive sentiment classification, and cause the UI to display a reply requesting the information to add to the prompt.

Abstract: At least one processor can receive at least one preliminary response generated by a machine learning (ML) model having a predetermined level of randomness. The at least one processor can determine at least one transformation applying a new level of randomness, different from the predetermined level of randomness, to the at least one preliminary response. The at least one processor can generate at least one modified preliminary response, the generating comprising applying the at least one transformation to the at least one preliminary response. The at least one processor can replace the at least one preliminary response with the at least one modified preliminary response within the ML model, wherein the ML model generates a final response using the at least one modified preliminary response.

Abstract: The present disclosure provides techniques for recommending targeted information. One example method includes receiving user data indicative of one or more attributes of one or more users and activity data indicating past actions by the one or more users in association with particular content items, identifying, using a first machine learning model, a topic based on the activity data, identifying, using a second machine learning model, a subset of attributes of the one or more attributes of the one or more users that are associated with the topic, generating a prompt based on the topic and the subset of attributes associated with the topic, and generating, based on the prompt using a large language model (LLM), content to provide to a user having the subset of attributes associated with the topic.

Abstract: A system and method for generating and optimizing marketing campaigns. More specifically, a campaign management system leverages a Large Language Model (LLM) to create multiple variations of an existing campaign tailored to specific target groups. The system employs a cluster-based approach and a click-through rate (CTR) prediction model to generate revised campaigns for targeted readers, thereby creating a feedback loop for further fine-tuning of the LLM for future campaigns.