A virtual assistant platform implemented by a computer system comprising: one or more hardware processors configured to execute computer readable instructions; one or more memory storing the instructions; a mapping data structure stored in the one or more memory, the mapping data structure mapping a plurality of intents to respective client specific actions; a network interface configured to receive a query from a user device operating in a client specific communication session with a virtual assistant in a first context, the instructions when executed providing: an AI language model comprising a client specific language model, the client specific language model having been trained on client specific data, and a mesh language model, the mesh language model having been trained on mesh specific data, the mesh specific data having been received by operating multiple virtual assistants in the first context, the AI language model being responsive to the query to generate an intent; a mapping function to apply the intent to the mapping data structure and access a corresponding client specific action for delivery of a response to the user device; and a transmission function to transmit the response to the user device.

In the information processing device, the selection means selects a template corresponding to a selected question item from a plurality of question items relating to a business, from a plurality of templates each indicating an instruction text for a document generation model. The generation means generates an instruction text in which answers to the plurality of question items are input in the selected template. The displaying means displays a reply to the generated instruction text from the document generation model.

Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).

According to one embodiment, an information processing apparatus comprising processing circuitry configured to: analyze data regarding genetic information according to a purpose of a data analysis and obtain analysis result data; and generate a prompt for inputting into a language model, the prompt instructing to interpret the analysis result data based on gene-related information related to the analysis result data and the purpose of the data analysis and obtain an interpretation result of the analysis result data based on the prompt and the language model.

System and method for generating one or more interactive responses for a computing device, said system comprising at least one processing unit connected to a memory. The method comprises generating one or more prompts to be displayed at the computing device. Thereafter, the method comprises receiving in real time one or more input queries from the at least one computing device. The method further comprises identifying one or more interactive attributes based on the one or more input queries and the one or more prompts. Lastly, one or more responses are generated to the one or more input queries and displayed at the display unit of the at least one computing device.

A method for recall model training includes: obtaining first text pairs with a first text pair including a first question text, generated based on description information of multimedia and using a resource identifier of the multimedia as a question target, and a first answer text, being a resource identifier targeted by a question of the first question text; pre-training a recall model; obtaining second text pairs with a second text pair including a second question text that uses a resource identifier of related multimedia as a question target, and a second answer text being a resource identifier of the related multimedia targeted by a question of the second question text, and the related multimedia involved in the second text pair corresponding to the multimedia involved in the first text pair; and performing fine tune training on the pre-trained recall model based on second question texts and second answer texts.

Systems, computer programs, devices, and methods that enable LLM-based user interfaces within real-time and/or embedded devices. Providing user-specific context to a generically trained LLM may enable a variety of new usages and scenarios. For example, adaptive prompt augmentation may enable a user device to augment user-generated prompts with additional user context in the form of machine-generated prompts. In some variants, machine-generated prompts may be further refined to accommodate e.g., foundation model constraints, etc. APIs for user-specific data structures can be used to e.g., optimize for habitual behaviors, user idiosyncrasies, etc. Agentic query construction may enable a user device to operate with autonomy and decision-making capabilities, beyond prompt-response interactions. Stitching (or dreaming) may be used to identify pattern-based associations within high dimensional space (embedding vectors).

The present disclosure herein addresses the problem of synthesizing a series of documents and extracting or summarizing meaningful information or content embedded as tacit knowledge in the series of documents. The embodiment of the present disclosure provides a system and method for tacit knowledge generation using large language model (LLM) in document synthesis. The method of the present disclosure performs intelligent document generation orchestrating a generative artificial intelligence solution workflow. In the present disclosure, tacit knowledge of subject matter experts in a knowledge base or in a series of documents is extracted. Further a content capturing the tacit knowledge is generated leveraging a large language models (LLMs) framework as the underlying architecture. The system of the present disclosure is artificial intelligence (AI) accelerated, cloud agnostic, latency defined, and security enabled.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a set of large language models to determine a natural language response to a query. One of the methods includes receiving a query related to a document. The document is submitted to a first model along with a prompt to generate an outline of the document. The document is submitted to a second model along with a prompt to generate metadata of the document. At least a portion of the query, document metadata, and the document outline are submitted to a third model with a prompt to generate a natural language response to the query. A selected sentence from the natural language response is correlated to a document sentence. The natural language response is provided to the user with an indication that the selected sentence from the natural language response is correlated to the document sentence.

One example method includes receiving a set of communication records, the set of communication records representing one or more communications between a first person and a second person; receiving a user query, the user query comprising one or more constraints; generating a plurality of segments from the communication records, at least a subset of the plurality of segments based on the one or more constraints; selecting one or more segments based on the one or more constraints; generating one or more queries based on the one or more constraints; providing, to a trained large language model, the one or more selected segments and the one or more generated queries; and receiving, from the trained LLM, and outputting a multidimensional analysis of the set of communication records.