An information processing method, by a processing unit of an information processing apparatus, includes: converting voice data into character string data; generating question data by extracting a first word from the character string data; extracting a second word from the character string data by inputting the character string data and the question data to a trained language learning model configured to output, when the character string data and the question data are input, a word corresponding to an answer to the question data from the character string data; and storing the voice data, the first word, and the second word in association with each other.

An apparatus comprises at least one processing device configured to obtain a query comprising search text and a context identifying documents to be searched using the search text, to generate document chunks by parsing the documents, to determine a degree of specificity of the search text, and to determine a number of the document chunks to select for retrieval-augmented generation processing based on the determined degree of specificity. The at least one processing device is also configured to select a subset of the document chunks based on similarity between the document chunks and the search text, the subset including the determined number of document chunks. The at least one processing device is further configured to generate and apply a prompt including the selected subset of the document chunks to a machine learning system to generate an output, and to provide an answer to the query based on the output.

The present disclosure provides a method of provisioning digital content in accordance with a regulation. Further, the method may include receiving one or more user request data from one or more user devices associated with one or more users. Further, the method may include analyzing the one or more user request data based on at one regulation data. Further, the one or more regulation data corresponding to a regulation of one or more communities. Further, the one or more users may be associated with the one or more communities. Further, the processing device implements an AI algorithm. Further, the method may include generating one or more response data based on the analyzing. Further, the one or more response data may be compliant with the one or more regulation data. Further, the method may include transmitting the one or more response data to the one or more user devices.

An iterative prompt training apparatus receives a search request for searching for information and automatically generates a first prompt instructing a large language model to search for the information requested in the search request. The iterative prompt training apparatus provides the first prompt to the large language model and analyzes a first search result, output by the large language model based on the first prompt, to determine whether an error exists in the first search result. In response to determining that the error exists in the first search result, the iterative prompt training apparatus automatically executes prompt adjustment to generate a second prompt that is different from the first prompt and provides the second prompt to the large language model.

A question response apparatus includes processing circuitry. The processing circuitry is configured to acquire a question input by a user generate a query based on the question search a search result related to the query from a plurality of information sources determine consistency of the search result generate an integration prompt in which a search result determined to be consistent and the question are integrated and generate an answer to the question by inputting the integration prompt into a large language model.

An information processing apparatus includes circuitry that receives an input of question data, outputs to a large-scale language model a prompt requesting generation of answer data to the question data based on user-specific information, and outputs to a terminal apparatus output answer data that is output from the large-scale language model in response to an input of the prompt.

Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents for input into a query response system, generate a set of vector embeddings based on the set of documents and a semantic vector augmentation pipeline, and generate a set of knowledge graphs based on the set of documents and a knowledge graph augmentation pipeline, where each knowledge graph includes a set of multiple knowledge graph triplets. The system may receive a user query and augment the user query to generate an augmented prompt using at least one or more vector embeddings from the set of vector embeddings and one or more knowledge graph triplets from the set of knowledge graphs. The system may provide, to a large language model (LLM), the augmented prompt as an input and may receive, as an output of the LLM, a response to the augmented prompt.

A method for retrieving information about an asset in an industrial plant includes providing a query and technical context information about at least one asset, to a large language model (LLM), obtain an answer to the query, wherein the context information relates to one or more of capabilities or requirements of the asset, how to interact with the asset, parameter values of the asset, and sensor data relating to the asset; setting up on the context information and the query and/or answer, a verification plan, the verification plan comprising one or more actions, wherein executing each action produces a confidence metric that is indicative of a propensity of the answer being correct; executing the verification plan, thereby obtaining confidence metrics; and determining, based on the confidence metrics, a propensity of the answer to the given query obtained from the LLM being correct.

A method for generating multi-modal response to a query using a generative machine learning model, the method including: receiving, from a client device, a query data object related to a sporting event; providing the query data object and a first prompt to a machine learning system; receiving, from the machine learning system, a function, from a set of functions, associated with the query data object; receiving, from the machine learning system, an output format; providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system, receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and outputting the response to one or more users.

Implementations relate to modifying one or more audio content playback settings of a client device. Processor(s) can receive one or more contextual signals associated with the client device or a user of the client device and can generate, based on at least the one or more contextual signals and a predefined context, a structured large language model (LLM) query. The processor(s) can generate, based on processing the structured LLM query, an LLM output that includes at least an indication of whether the client device or the user is in the predefined context. The processor(s) can determine, based on processing the indication, whether the client device or the user device is in the predefined context and, responsive to determining that the client device or the user is in the predefined context, modify one or more audio content playback settings of the client device for rendering audio content.