U.S. Bancorp, National Association

U.S. Bank National Association

Samuel Assefa

Sherin Mathews

Giacomo Domeniconi

Ahmed Mohammed Abdelrahman Mohammed

Anand K Yadav

Charles Engdahl

David Ridgway

Ahmed Abdelrahman

Andrew Garner

A system includes one or more processors to receive a natural language query; execute the first language model using the natural language query to generate a text-based response to the natural language query; segment the text-based response into a plurality of segments each corresponding to a different fact-based response to the natural language query; convert each of the segments into a set of queries; generate a query response to each query by using the query to search a data repository; generate a factuality score of the query responses for the text-based response to the natural language query; generate an aggregate factuality score for the natural language query based on the factuality score for each query generated for each of the plurality of segments; and executing decontextualized atomic decomposition responsive to determining the factuality score does not satisfy a threshold.

Remediating hallucinations in language models

An embodiment detects, in a structured data corresponding to a graphical user interface (UI) design, a UI component. The embodiment determines, using a compliance specification retrieved from a compliance system repository, that the UI component is non-compliant. The embodiment extracts a code block from the compliance system repository, where the code block is executable to render a compliant component in place of the UI component on a UI rendering from the UI design. The embodiment combines, to automatically output a front-end UI code, the code block with generated code, the generated code corresponding to another element of the UI design, and the front-end UI code rendering a complete UI page corresponding to the UI design.

Automatic generation of compliant user interface code

A method and related systems for providing adaptive responses by varying parameters used to select content includes obtaining an accuracy feedback indicator associated with a first generated message and determine an updated context generation component by applying a feedback-based protocol to the first state to set the control parameters to a second state based on the accuracy of the first generated message. Some embodiments may then generate a content sequence using the modified control parameters and use the content sequence as an input context for a second query to a language model.

Control parameter feedback protocol for adapting to data stream response feedback

A mobile electronic device includes a network interface and one or more processors coupled to memory. The one or more processors can be configured to receive a message comprising transaction data regarding a transaction and a request regarding activating a chat application stored in the memory; the chat application, wherein the chat application is configured to execute a large language model using the transaction data and account data as input to generate a first string of text requesting further data regarding the transaction; present the first string of text at a chat interface of the chat application; receive additional transaction data; execute the large language model using the additional transaction data as input to generate a second string of text indicating storage of the additional data in a record; and present the second string of text at the chat interface.

Systems and methods for contextual transaction data collection using large language processing

A method comprises transmitting a first client model to a first computing device and a second client model to a second computing device; determining (i) a first predictive score ratio for the first computing device, and (ii) a first predictive score ratio for the second computing device; determining the first computing device and the second computing device match; determining (i) a second predictive score ratio for the first computing device, and (ii) a second predictive score ratio for the second computing device; and detecting an anomaly in the first computing device responsive to (i) the determining the first computing device and the second computing device match, and (ii) determining the second predictive score ratio for the first computing device exceeds the second predictive score ratio for the second computing device by an amount above a difference threshold.

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Watertown, MA, United States of America

Samuel Assefa