Abstract: Personally-stylized content can be generated without fine tuning a model. A personally-stylized content generation method can include receiving a first request for first content to be stylized in a style of written prose previously produced by a user, applying a previously trained retriever model to the first request to obtain second content previously produced by the user resulting in obtained content, populating a prompt with the obtained content and the first request resulting in an augmented prompt, providing the augmented prompt to a large language model (LLM), receiving personally-stylized content from the LLM, the personally-stylized content including elements of the style of the written prose of the user, and providing the personally-stylized content to the user.

Abstract: Systems and methods for open-domain dialogue segmentation and state tracking are provided. In particular, a computing device may obtain and analyze a dialogue in near real-time, generate a structured prompt template for a state prediction model based on the dialogue, and generate a structured output using the state prediction model based on the structured prompt template. The structured output includes a turn summary and state labels for each dialogue turn.

Abstract: Various embodiments discussed herein relate to prompting a model, such as a Large Language Model (LLM), to ingest natural language clustering instructions and generate corresponding natural language clustering information, such as a cluster description and/or a cluster label without the need to generate any numeric text embeddings.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for efficiently generating and using intent taxonomies. In embodiments, training data, including data requests for information, is obtained. Thereafter, a model prompt to be input into a large language model is generated. The model prompt includes an instruction to generate an intent taxonomy, an indication of the training data to use for generating the intent taxonomy, and a taxonomy attribute desired to be used as criteria to generate a quality intent taxonomy. An intent taxonomy that includes user intent classes is obtained as output from the large language model. The intent taxonomy is analyzed to determine whether the intent taxonomy is valid. When the intent taxonomy is determined as valid, the intent taxonomy is provided for use in identifying user intent, and when the intent taxonomy is determined as invalid, the intent taxonomy is refined.

Abstract: A system and method and for optimizing cross-team information flow in a communication network includes receiving, from a communication application, via a network, a plurality of candidate post items for display to a first user of an organization, each candidate post item being a post item published by another user of the organization and being a post that is accessible to the first user. A communication knowledge network graph which represents communication events that have occurred between users of the organization is then generated where each communication event is represented by a first node that represents a sender, a second node that represents a receiver and an edge that represents the communication event from the sender to the receiver.

Abstract: An enterprise system server, a computer-readable storage medium, and a method for targeted training of inductive multi-organization recommendation models for enterprise applications are described herein. The method includes receiving enterprise application data from remote organization computing systems executing the enterprise application, training per-organization recommendation models for a subset of the organizations, and validating each per-organization recommendation model on enterprise application data corresponding to one or more other organizations.

Abstract: A computing system, computer-readable storage medium, and method for individual treatment effect (ITE) estimation under high-order interference in hypergraphs are described herein. The method includes accessing, via a processor, a hypergraph dataset including multi-way interactions among nodes within each hyperedge of a corresponding hypergraph, where the hypergraph dataset corresponds to a treatment assignment for each node. The method includes performing representation learning on the hypergraph dataset to control for confounders corresponding to features of each node and to learn a confounder representation for each node. The method also includes modeling a high-order interference representation for each node by propagating the learned confounder representation and the treatment assignment for each node through a hypergraph neural network.

Abstract: An enterprise system server, a computer-readable storage medium, and a method for targeted training of inductive multi-organization recommendation models for enterprise applications are described herein. The method includes receiving enterprise application data from remote organization computing systems executing the enterprise application, training per-organization recommendation models for a subset of the organizations, and validating each per-organization recommendation model on enterprise application data corresponding to one or more other organizations.