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 18444078 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.

Key Features and Innovation:

System equipped with hardware processors
Utilizes a pre-trained large language model to respond to search queries
Fine-tunes the model to create a task-specific generative model
Analyzes search results based on performance metrics
Refines the generative model based on search result analysis

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 results for user queries.

Benefits:

Enhanced user experience with more precise search results
Improved efficiency in information retrieval
Tailored responses to specific search queries

Commercial Applications: The technology can be utilized in search engine optimization tools, online advertising platforms, and content recommendation systems.

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

Frequently Updated Research: Stay informed about the latest advancements in large language models, search algorithms, and information retrieval systems.

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

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.