18135051. CONTEXTUAL KNOWLEDGE SUMMARIZATION WITH LARGE LANGUAGE MODELS simplified abstract (Microsoft Technology Licensing, LLC)

CONTEXTUAL KNOWLEDGE SUMMARIZATION WITH LARGE LANGUAGE MODELS

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Sebastian Johannes Blohm of Munich (DE)

Dmitriy Meyerzon of Bellevue WA (US)

Aaron Lee Halfaker of Seattle WA (US)

James John Hensman of Cambridge (GB)

CONTEXTUAL KNOWLEDGE SUMMARIZATION WITH LARGE LANGUAGE MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18135051 titled 'CONTEXTUAL KNOWLEDGE SUMMARIZATION WITH LARGE LANGUAGE MODELS

The techniques disclosed in this patent application aim to improve the efficiency and functionality of knowledge base systems by automating the generation of knowledge base content, such as topic definitions, using a large language model.

• Summarization module processes incoming requests related to a knowledge base topic
• Generates instructions for a large language model to produce natural language output
• Ensures consistency and relevance of outputs from the large language model
• Maintains information security for content generated from privileged information
• Allows for review and editing of large language model outputs for accuracy

Potential Applications: - Enhancing the efficiency of knowledge base systems - Improving the quality of automated content generation - Enhancing information security in knowledge base systems

Problems Solved: - Streamlining the process of generating knowledge base content - Ensuring consistency and relevance of generated content - Maintaining information security for privileged information

Benefits: - Increased efficiency in knowledge base systems - Improved accuracy of generated content - Enhanced information security measures

Commercial Applications: Title: Automated Knowledge Base Content Generation System This technology can be used in various industries such as customer service, education, and research to automate the generation of knowledge base content, improving efficiency and accuracy in information dissemination.

Questions about Automated Knowledge Base Content Generation System: 1. How does this technology impact the overall efficiency of knowledge base systems?

  - This technology significantly improves the efficiency of knowledge base systems by automating the generation of content, reducing manual effort and time required for creating and updating information.

2. What are the potential security implications of using a large language model to generate content?

  - The use of a large language model for content generation may raise concerns about maintaining information security, especially when dealing with privileged or sensitive information. Implementing access controls and review processes can help mitigate these risks.

Original Abstract Submitted

The techniques disclosed herein enable systems to enhance the efficiency and functionality of knowledge base systems through automated generation of knowledge base content such as topic definitions using a large language model. This is accomplished by utilizing a summarization module that processes incoming requests pertaining to a knowledge base topic. In response to a request, the summarization module can retrieve information related to the topic and generate an instruction directing a large language model to generate a natural language output. By generating the instruction from the specific context of the knowledge base, the disclosed techniques can ensure that outputs received from the large language model are consistent and relevant. In addition, content that was generated based on privileged information such as an access-controlled document can receive the same access controls to maintain information security. Furthermore, large language model outputs can undergo a review and editing process to ensure accuracy.