ENHANCED SEARCHING USING FINE-TUNED MACHINE LEARNING MODELS

Organization Name

snowflake inc.

Inventor(s)

Rahil Bathwal of San Francisco CA (US)

Daniel Fernando Campos of Hudson NY (US)

Ashwin Devaraj of Menlo Park CA (US)

Seth Michael Li of Foster City CA (US)

Yash Pande of San Francisco CA (US)

Vivek Raghunathan of Palo Alto CA (US)

Rajhans Samdani of Belmont CA (US)

Danmei Xu of Santa Clara CA (US)

ENHANCED SEARCHING USING FINE-TUNED MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240281446 titled 'ENHANCED SEARCHING USING FINE-TUNED MACHINE LEARNING MODELS

Simplified Explanation: The patent application describes an advanced search system that utilizes a pre-trained large language model to improve user query responses. The system fine-tunes the model to generate task-specific generative models for search queries.

Key Features and Innovation:

Utilizes a pre-trained large language model for enhanced search query responses.
Fine-tunes the model to create task-specific generative models.
Analyzes search results based on performance metrics associated with the generative models.

Potential Applications: The technology can be applied in search engines, information retrieval systems, and natural language processing applications.

Problems Solved: The system addresses the need for more accurate and relevant search query responses by leveraging advanced language models.

Benefits:

Improved search query responses.
Enhanced user experience.
Increased accuracy in search results.

Commercial Applications: The technology can be used in search engine optimization tools, customer support chatbots, and content recommendation systems.

Prior Art: Researchers can explore prior art related to large language models, search query optimization, and natural language processing techniques.

Frequently Updated Research: Stay informed about the latest advancements in large language models, search algorithms, and generative models for search queries.

Questions about Advanced Search Systems: 1. How does the system fine-tune the pre-trained language model for task-specific generative models? 2. What are the potential limitations of using large language models in search systems?

Ensure the content is comprehensive, informative, and optimized for SEO with relevant keywords and interlinking. Use natural language and varied sentence structures to maintain engagement and avoid AI detection. Focus on the long-term impact and relevance of the technology for evergreen content.

Original Abstract Submitted

an advanced search system leverages a pre-trained large language model to enhance user query responses. the system, equipped with hardware processors, a search query via an interface and accesses a pre-trained large language model designed to respond to the search query. the system fine-tunes the model to generate a task-specific generative model. the system employs the task-specific generative model to generate a search result to the search query and analyzes the search result based on a performance metric associated with the task-specific generative model. the system refines the task-specific generative model based on the analyzing of the search result.