18364355. PERSONALIZED MULTI-RESPONSE DIALOG GENERATED USING A LARGE LANGUAGE MODEL simplified abstract (Google LLC)
Contents
PERSONALIZED MULTI-RESPONSE DIALOG GENERATED USING A LARGE LANGUAGE MODEL
Organization Name
Inventor(s)
Anoop K. Sinha of Palo Alto CA (US)
Jason S. Spielman of Los Altos CA (US)
PERSONALIZED MULTI-RESPONSE DIALOG GENERATED USING A LARGE LANGUAGE MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18364355 titled 'PERSONALIZED MULTI-RESPONSE DIALOG GENERATED USING A LARGE LANGUAGE MODEL
The abstract describes techniques for generating personalized multi-response dialog using large language models. The method involves receiving natural language input from a client device, generating responses using a large language model, determining three responses, scoring the responses, selecting a subset of responses, and rendering them on the client device.
- Receiving natural language input from a client device
- Generating responses using large language models
- Determining three responses to the input
- Scoring the responses based on criteria
- Selecting a subset of responses with high scores
- Rendering the selected responses on the client device
Potential Applications: - Chatbots for customer service - Personalized virtual assistants - Interactive storytelling applications
Problems Solved: - Enhancing user engagement in dialog systems - Providing more relevant and personalized responses - Improving user experience with AI-powered interactions
Benefits: - Increased user satisfaction - More efficient communication - Enhanced user-device interaction
Commercial Applications: Title: "Enhancing User Engagement with AI-Powered Dialog Systems" This technology can be used in customer service chatbots, virtual assistants for personalized assistance, and interactive storytelling applications. The market implications include improved user experience, increased customer satisfaction, and potential cost savings for businesses.
Questions about the technology: 1. How does this technology improve user engagement in dialog systems? 2. What are the potential commercial uses of personalized multi-response dialog systems?
Frequently Updated Research: Stay updated on advancements in large language models and their applications in dialog systems for improved user interactions.
Original Abstract Submitted
Techniques are described herein for personalized multi-response dialog generated using one or more large language models. A method includes: receiving first natural language (NL) based input associated with a client device; generating, based on the first NL based input and using at least one large language model (LLM), one or more instances of first LLM output; determining, based on the one or more instances of first LLM output, at least three responses to the first NL based input; determining, based on at least one scoring criterion, respective scores of the at least three responses to the first NL based input; selecting, based on the respective scores of the at least three responses to the first NL based input, from the at least three responses to the first NL based input, a first subset, the first subset comprising at least two responses to the first NL based input; and causing each of the at least two responses in the first subset to be rendered at the client device.
