Google llc (20240289395). FACTUALITY OF GENERATED RESPONSES simplified abstract

FACTUALITY OF GENERATED RESPONSES

Organization Name

google llc

Inventor(s)

Hao Zhou of Redwood City CA (US)

Shrestha Basu Mallick of San Francisco CA (US)

Trevor Strohman of Menlo Park CA (US)

Patricia Luisa Romero Domingo of Jackson Heights NY (US)

Amirhossein Kiani of San Francisco CA (US)

Yu Du of Sunnyvale CA (US)

Xinying Song of Kirkland WA (US)

Heng-Tze Cheng of Mountain View CA (US)

Quoc V. Le of Palo Alto CA (US)

Ed Huai-Hsin Chi of Palo Alto CA (US)

Christopher Jamie Maclean Hall of Tempe (AU)

FACTUALITY OF GENERATED RESPONSES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240289395 titled 'FACTUALITY OF GENERATED RESPONSES

The patent application relates to assisting a large language model in generating factual responses to prompts that require factual information. The model receives a prompt context and encoded context passages as input, determining whether to use the encoded passages in generating the response. Various methods are employed to fine-tune the responses, including query refinements, response re-writes, and factual accuracy evaluation.

• Large language model generates factual responses to prompts
• Receives prompt context and encoded context passages as input
• Determines whether to use encoded passages in response generation
• Fine-tunes responses through query refinements, response re-writes, and factual accuracy evaluation

Potential Applications: - Content generation for educational platforms - Fact-checking tools for news organizations - Automated customer support chatbots

Problems Solved: - Enhancing the accuracy of factual responses generated by language models - Improving the efficiency of content creation processes - Providing reliable information in real-time scenarios

Benefits: - Increased speed and accuracy in generating factual content - Reduction in manual fact-checking efforts - Enhanced user experience through more informative responses

Commercial Applications: Title: "Enhancing Factual Content Generation with Large Language Models" This technology can be utilized in content creation platforms, customer service chatbots, and knowledge management systems to improve the quality and efficiency of information dissemination.

Questions about Large Language Models: 1. How do large language models determine the relevance of encoded context passages in generating responses? 2. What are the potential challenges in fine-tuning responses generated by large language models?

Frequently Updated Research: Stay updated on advancements in large language models, natural language processing, and information retrieval techniques to enhance the capabilities of this technology.

Original Abstract Submitted

implementations relate to helping a large language model generate factual responses to prompts that request factual content is disclosed. the large language model may receive a prompt context, a plurality of encoded context passages as input. the large language model is trained to determine whether or not to utilize the encoded context passages in generating the response. implementations also relate to different methods of fine-tuning the responses generated by the large language model through query refinements, response re-writes, and evaluation of factual accuracy.