Microsoft technology licensing, llc (20240346254). NATURAL LANGUAGE TRAINING AND/OR AUGMENTATION WITH LARGE LANGUAGE MODELS simplified abstract

NATURAL LANGUAGE TRAINING AND/OR AUGMENTATION WITH LARGE LANGUAGE MODELS

Organization Name

microsoft technology licensing, llc

Inventor(s)

Yang Liu of Bellevue WA (US)

Yichong Xu of Bellevue WA (US)

Dan Iter of Austin TX (US)

Chenguang Zhu of Bellevue WA (US)

Nanshan Zeng of Bellevue WA (US)

Shuohang Wang of Belevue WA (US)

Hiteshi Sharma of San Jose CA (US)

NATURAL LANGUAGE TRAINING AND/OR AUGMENTATION WITH LARGE LANGUAGE MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240346254 titled 'NATURAL LANGUAGE TRAINING AND/OR AUGMENTATION WITH LARGE LANGUAGE MODELS

The techniques described in this patent application aim to improve natural language generation systems by leveraging a large language model for training and augmentation purposes.

• The large language model can train the natural language generation system by processing a training dataset to generate natural language outputs.
• The system can then analyze the training dataset and the generated outputs to mimic the large language model's output.
• The large language model can evaluate the system's outputs to iteratively enhance the quality of natural language outputs.
• Additionally, the large language model can augment a small language model by providing context and a language framework to improve overall outputs.

Potential Applications: - Enhancing the performance of natural language generation systems in various industries such as customer service, content creation, and chatbots. - Improving the accuracy and efficiency of language processing tasks in automated systems.

Problems Solved: - Addressing the need for more effective training and augmentation techniques in natural language generation systems. - Enhancing the capabilities of small language models by leveraging a large language model.

Benefits: - Improved accuracy and quality of natural language outputs. - Increased efficiency in language processing tasks. - Enhanced performance of automated systems utilizing natural language generation.

Commercial Applications: - This technology could be applied in customer service chatbots to provide more accurate and human-like responses. - Content creation platforms could benefit from improved natural language generation capabilities for generating articles, product descriptions, and more.

Questions about the Technology: 1. How does this technology compare to traditional methods of training natural language generation systems? 2. What are the potential limitations of using a large language model for augmentation in small language models?

Frequently Updated Research: - Stay updated on advancements in large language models and their applications in natural language generation systems for potential improvements in performance and efficiency.

Original Abstract Submitted

the techniques described herein enhance the operations of natural language generation systems through training and/or augmentation by a large language model. in a first example, the large language model can execute training operations by processing a training dataset to produce a natural language output. the natural language generation system can analyze the training dataset and the natural language output to generate a natural language output mimicking the output of the large language model. the large language model can then evaluate the output of the natural language generation system to iteratively adjust and improve the quality of natural language outputs. in a second example, the large language can augment a small language model in executing natural language tasks. this is accomplished by retrieving external information using the large language model to generate an augmentation input to provide context and a language framework to the small language model to enhance overall outputs.