Systems and methods for partial information retrieval using data provenance techniques are disclosed. The system includes an partial information retrieval processor that executes an event trigger software agent which identifies an event, a query listener agent which generates a query in response to the identified event, and a partial information retrieval agent which processes the query in accordance with one or more modular domain heuristic data structures and generates response data that includes provenance information. The system can include a knowledge base updating agent which updates a knowledge base using the response data, as well as a response generator agent. The system allows for the generation of natural language answers to questions in circumstances where only partial information is available, such as partially-identified question types or question contexts. A visualization user interface is also provided, which allows for visualization of partial information retrieval outcome.

A method includes receiving natural language input text characterizing clinical data for a patient. The method also includes receiving a prompt composition that includes adjudication rules for performing lines of therapy adjudication and an instruction parameter that specifies a task for a LLM to synthesize a group of multiple synthetic experts that each use the adjudication rules to perform chain-of-thought reasoning for making lines of therapy (LoT) adjudication decisions. The method also includes structuring an adjudication prompt by concatenating the prompt composition to the natural language input text, processing, using the LLM, the adjudication prompt to cause the LLM to synthesize the group of multiple synthetic experts and generate a respective group answer. The method also includes determining a final answer based on the respective group answer generated from the group of multiple synthetic experts.

The present disclosure provides an information display method and apparatus, a computer device, and a storage medium. The method includes: displaying a target video and artificial intelligence question-answering prompt information; displaying an artificial intelligence dialogue interface in response to a trigger operation for the artificial intelligence question-answering prompt information; and displaying, on the artificial intelligence dialogue interface, generated first question information associated with the target video and a generated first artificial intelligence answer result corresponding to the first question information.

A method and system for interacting with a building management system via a chatbot includes receiving a natural language query and processing it to identify tools and arguments needed to collect information from data sources associated with the building management system. The identified tools are executed with corresponding arguments to collect information from the data sources. The collected information and natural language query are submitted to a Large Language Model, which generates a natural language response based on the query and collected information.

An information processing apparatus according to the present application includes a reception unit, a selection unit, and a providing unit. The reception unit receives a query including a prompt sent from a user or a query for obtaining the prompt. The selection unit selects, based on information on a plurality of items included in the prompt, AI that is used to generate response information indicating a response to the prompt from among a plurality of pieces of AI. The providing unit provides the response information that has been generated by using the AI selected by the selection unit to the user.

Embodiments of the present application provide an object material generation method, system, model fine-tuning method, electronic device, and storage medium. The object material generation method includes: performing intent recognition on instruction information indicating the generation of an object material to obtain an intent recognition result, wherein the intent recognition result includes a material generation scenario and/or key information of the object; in response to the intent recognition result meeting a preset condition, formatting a preset prompt template based on the intent recognition result to generate a prompt; in response to the intent recognition result not meeting the preset conditions, generating a prompt based on the instruction information using a pre-fine-tuned prompt generation model; triggering a preset object material generation model based on the generated prompt to produce the object material.

Aspects of the present application relate to telemetry data processing using generative machine learning (ML). In examples, a prompt is generated that induces the generative ML model to interpret telemetry data according to the prompt. For instance, the prompt includes a semantic event index that defines a set of events relating to one or more issues, wherein each event is associated with a description and/or other context information for the event. An indication of the telemetry data may thus be provided for processing by the generative ML model. The generative ML model generates model output relating to the telemetry data, for example responsive to natural language input. Accordingly, the disclosed aspects may enable a developer or other user to converse with the generative ML model about telemetry data as though the model were the user.

Systems and techniques may generally be used for chatbot-based fraud assistance. An example method may include initiating a chatbot session with a user and receiving a prompt from the user related to suspected suspicious activity in an account. The method may include retrieving, using a Retrieval-Augmented Generation (RAG) component, contextual information from at least one of transaction data and a knowledge base. The method may include evaluating the prompt using a large language fraud model and the retrieved contextual information to determine a response.

A method of determining a natural language output regarding a digital network using a large language model (LLM) can include formulating a desired output dependent upon information associated with the digital network; providing, to the LLM, the information associated with the digital network and a first prompt requesting the LLM to generate a query dependent upon the information and the desired output; receiving, from the LLM, the query dependent upon the information and the desired output; determining, dependent upon a graph database, a response to the query with the graph database being representative of at least a portion of the digital network; providing, to the LLM, the response and a second prompt requesting the LLM to generate the natural language output dependent upon the response; and receiving, from the LLM, the natural language output dependent upon the response and associated with the digital network.

A method of interacting with a large language model to elicit a semantic feature of interest from a document under review includes electronically inputting, in an application program interface of a chat application, a first prompt assigned to a user, the first prompt yielding a plurality of possible responses from the language model based on content of the document under review; generating an example set comprising text from example documents representative of each of the plurality of possible responses; and electronically inputting, before the first prompt in an application program interface of a chat application, a fabricated history of a conversation between the user and the language model. The fabricated history includes the example set and a plurality of possible responses assigned to the language model.