View source for 18328610. ENRICHING LANGUAGE MODEL INPUT WITH CONTEXTUAL DATA simplified abstract (Microsoft Technology Licensing, LLC)

=ENRICHING LANGUAGE MODEL INPUT WITH CONTEXTUAL DATA=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Haim Somech of Ramat Gan (IL)|Haim Somech of Ramat Gan (IL)]][[Category:Haim Somech of Ramat Gan (IL)]]

[[:Category:Adi L. Miller of Ramat Hasharon (IL)|Adi L. Miller of Ramat Hasharon (IL)]][[Category:Adi L. Miller of Ramat Hasharon (IL)]]

[[:Category:Assaf Avihoo of Matan (IL)|Assaf Avihoo of Matan (IL)]][[Category:Assaf Avihoo of Matan (IL)]]

==ENRICHING LANGUAGE MODEL INPUT WITH CONTEXTUAL DATA - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18328610 titled 'ENRICHING LANGUAGE MODEL INPUT WITH CONTEXTUAL DATA

The abstract of the patent application discusses the use of a corpus data supplement to improve the accuracy of predictions made by a Large Language Model (LLM) by helping the model distinguish between general natural language concepts and domain-specific concepts.

* The innovation involves providing a corpus data supplement as input into a model, such as a Large Language Model (LLM), to enhance the model's ability to generate accurate scores or data for predictions.
* By using the corpus data supplement, the model can better differentiate between general natural language concepts and domain-specific concepts, leading to more precise predictions.
* This technology aims to improve existing technologies by enhancing the performance of models like LLMs in understanding and processing language data.

Potential Applications:
This technology can be applied in various fields such as natural language processing, machine learning, artificial intelligence, and data analysis.
It can be used in chatbots, virtual assistants, language translation tools, sentiment analysis, and content generation applications.

Problems Solved:
Enhances the accuracy of predictions made by language models.
Improves the understanding of domain-specific concepts in natural language processing.
Addresses the challenge of distinguishing between general language concepts and specialized terminology.

Benefits:
Increased accuracy in predicting language data.
Enhanced performance of language models in processing domain-specific information.
Improved efficiency in generating content and analyzing language data.

Commercial Applications:
This technology can be utilized in developing advanced chatbots for customer service applications.
It can be integrated into language translation tools to improve accuracy and speed.
Market Implications:
The technology can lead to more efficient and accurate language processing applications, enhancing user experience and productivity.

Questions about the Technology:
1. How does the use of a corpus data supplement improve the performance of Large Language Models?
2. What are the potential applications of this technology beyond natural language processing?


==Original Abstract Submitted==

Various embodiments discussed herein are directed to improving existing technologies by providing a corpus data supplement as input into a model, such as a Large Language Model (LLM). Consequently, the model can generate accurate scores or data for predictions because the model is better able to distinguish between a general understanding of natural language concepts and domain-specific concepts.



[[Category:G06F40/279]]
[[Category:G06N20/00]]
[[Category:CPC_G06F40/279]]