Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

A computing system monitors inference conditions of a generative model, detects a predetermined trigger condition among the monitored inference conditions, and responsive to detecting the predetermined trigger condition, consolidates a memory vector database of the generative model to thereby extract semantic memories from the vector database, updates the generative model using the extracted semantic memories, and deploys the consolidated generative model. The predetermined trigger condition may be at least one of a database size condition, an available memory size condition, a processor load condition, or a scheduled time condition.

An information processing device of an embodiment includes a processing circuit. The processing circuit acquires a reply from a user. The processing circuit estimate medical treatment information including a plurality of preferences of the user regarding medical treatment on the basis of the reply. The processing circuit determine a response to the reply on the basis of the medical treatment information. The processing circuit outputs the response via an output interface. Furthermore, the processing circuit determines, as the response, at least one of a narrowed response and an approval request to the user.

An embodiment includes a method of augmenting performance and compliance of language model-based copilots. The method includes receiving application-specific guidance providing instructions that restrict responses output by an application-specific copilot based on a large language model (LLM). The method includes communicating to the LLM the application-specific guidance and setting an initial set of model parameters for the LLM. The method includes sequentially optimizing model parameters related to multiple model output characteristics of the LLM to generate a final set of model parameters. The method includes communicating the final set of model parameters to the LLM such that the final set of model parameters is implemented in the LLM during operations implemented by the copilot. The method includes deploying the copilot in an environment such that the copilot receives an actual query and replies with an actual response based on the LLM implementing the final set of model parameters.

The present invention relates to a system and a method implemented by an intelligent module. The system comprises an interface, an artificial intelligence module, and an intelligent flow framework module. The intelligent flow framework module is communicatively coupled to the interface and the artificial intelligence module. The intelligent flow framework module is configured to define at least one task based on an event and contextual data for completing a mission. The system provides the ability to adapt quickly to changing circumstances and make intelligent decisions to ensure the successful completion of missions/objectives.

An information processing apparatus according to the present application includes a reception unit that receives information indicating a request of a user, a determination unit that determines whether to generate information indicating a response to the request indicated by the received information, and a providing unit that provides the user with response information generated by using a generative AI as the information indicating the response to the request in a case where determining to generate the information indicating the response. The determination unit determines whether to generate the information indicating the response to the request indicated by the information received by the reception unit on the basis of whether the request indicated by the information received by the reception unit is a request related to a first target set as a non-response target and a request related to a second target set as a response target.

An information processing apparatus according to the present application includes a reception unit, a determination unit, and a providing unit. The reception unit receives a query including a new prompt sent from a user or a query for obtaining the new prompt. The determination unit determines whether or not a response to the new prompt is available by using existing information that is already-existing information. The providing unit provides, when it is determined by the determination unit that the response to the new prompt is available by using the existing information, the existing information to the user as response information indicating the response to the new prompt.

Systems, methods, and computer-readable media for mapping third-party specific question-and-answer pairs to standardized insurance-related question-and-answer pairs. A system may communicate with a third party and may receive third-party specific question-and-answer pairs. The system may include a rules management framework for mapping third-party specific question-and-answer pairs to standardized question-and-answer pairs. The rules management framework may interface with a library for housing the standardized question-and-answer pairs. The rules management framework may interface with a machine learning model to generate a similarity score between the third-party specific question-and-answer pairs and the standardized question-and-answer pairs. The system may include an ordering engine for ordering the questions of the standardized question-and-answer pairs. The system may include an error detection module for flagging when a third party indicates an error is present in the standardized question-and-answer pairs.

A method and a system for triggering an intelligent dialogue through an audio-visual reality. In the method, a reality image interface is initiated in a user device, a camera is activated to obtain an environment image, and a microphone is activated to obtain an environment audio. Then, a cloud server receives location information and a reality image request from the user device, obtains an environment object by identifying the environment image and an environment sound by identifying the environment audio. Afterwards, an intelligent dialogue link point displayed on the reality image interface is triggered to activate an intelligent dialogue program. An intelligent dialogue interface is initiated in the intelligent dialogue program and a chatbot is introduced in the intelligent dialogue program. The chatbot runs a natural language model to generate a dialogue content based on the location information, the environment object, and the environment sound.

Disclosed is an approach for transitioning between a main topic state and a tangential topic state in a virtual artificially intelligent (AI) system. A state machine controlling a directed conversation is received by the AI system. A knowledge base is ingested for the directed conversation by the AI system. A first input from a user is processed by the AI system which causes the AI system to enter a first state related to a first topic. Responsive to receiving a second input, by the AI system from the user not related to the first topic transitioning, by the AI system, into a tangential topic state.