18136634. STREAMING OF NATURAL LANGUAGE (NL) BASED OUTPUT GENERATED USING A LARGE LANGUAGE MODEL (LLM) TO REDUCE LATENCY IN RENDERING THEREOF simplified abstract (Google LLC)

STREAMING OF NATURAL LANGUAGE (NL) BASED OUTPUT GENERATED USING A LARGE LANGUAGE MODEL (LLM) TO REDUCE LATENCY IN RENDERING THEREOF

Organization Name

Google LLC

Inventor(s)

Martin Baeuml of Wollerau (CH)

Yanping Huang of Mountain View CA (US)

Wenhao Jia of Saratoga CA (US)

Chang Lan of Kirkland WA (US)

Yuanzhong Xu of Mountain View CA (US)

Junwhan Ahn of San Jose CA (US)

Alexander Bailey of Wollerau (CH)

Leif Schelin of Zurich (CH)

Trevor Strohman of Sunnyvale CA (US)

Emanuel Taropa of Los Altos CA (US)

Sidharth Mudgal of Mountain View CA (US)

Yanyan Zheng of Palo Alto CA (US)

Zhifeng Chen of Sunnyvale CA (US)

Ahmad Beirami of New York NY (US)

STREAMING OF NATURAL LANGUAGE (NL) BASED OUTPUT GENERATED USING A LARGE LANGUAGE MODEL (LLM) TO REDUCE LATENCY IN RENDERING THEREOF - A simplified explanation of the abstract

This abstract first appeared for US patent application 18136634 titled 'STREAMING OF NATURAL LANGUAGE (NL) BASED OUTPUT GENERATED USING A LARGE LANGUAGE MODEL (LLM) TO REDUCE LATENCY IN RENDERING THEREOF

Simplified Explanation: The patent application relates to reducing latency in generating and rendering natural language output using a large language model.

Key Features and Innovation:

• Processors receive natural language input from a client device and generate natural language output using a large language model.
• The output is generated on a segment-by-segment basis to reduce latency in evaluating and rendering the output.
• The first segment of the output is selected for inclusion in the stream of output while subsequent segments are being generated to further reduce latency.

Potential Applications: This technology can be applied in chatbots, virtual assistants, customer service automation, and content generation platforms.

Problems Solved: This technology addresses the issue of latency in generating and rendering natural language output from large language models.

Benefits:

• Improved efficiency in generating natural language output.
• Reduced latency in rendering the output.
• Enhanced user experience with faster response times.

Commercial Applications: The technology can be used in customer service chatbots, automated content creation tools, and virtual assistants to provide quicker and more accurate responses to users.

Prior Art: Prior research in natural language processing and machine learning can provide insights into similar approaches to reducing latency in generating and rendering natural language output.

Frequently Updated Research: Stay updated on advancements in natural language processing, machine learning, and artificial intelligence to enhance the efficiency and effectiveness of this technology.

Questions about Latency Reduction in Natural Language Generation: 1. How does reducing latency in natural language generation benefit users? 2. What are the potential challenges in implementing this technology in real-time applications?

By focusing on reducing latency in generating and rendering natural language output, this technology aims to enhance the speed and accuracy of communication in various applications.

Original Abstract Submitted

Implementations relate to reducing latency in generating and/or rendering natural language (NL) output generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, and generate the NL based output utilizing the LLM. The NL based output can be a stream of NL based output in that it includes a plurality of segments, and is generated on a segment-by-segment basis. In some implementations, a first segment of the stream of NL based output is selected for inclusion in the stream of NL based output as a second segment (and any subsequent segment) is being generated to reduce latency in evaluating the NL based output as a whole prior to rendering thereof. In some versions of those implementations, the first segment is rendered as the second segment (and any subsequent segment) is being generated to further reduce latency in rendering thereof.