18133938. NATURAL LANGUAGE TRAINING AND/OR AUGMENTATION WITH LARGE LANGUAGE MODELS simplified abstract (Microsoft Technology Licensing, LLC)

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 18133938 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 natural language generation system analyzes the training dataset and output to mimic the large language model's output, allowing for iterative adjustments to enhance output quality.
• The large language model can also augment a small language model by providing context and a language framework through external information retrieval, improving overall outputs.

Key Features and Innovation:

• Training natural language generation systems using a large language model for improved output quality.
• Augmenting small language models with external information retrieved by the large language model for enhanced performance.

Potential Applications:

• Enhancing chatbots and virtual assistants with more natural and contextually relevant responses.
• Improving automated content generation for various applications such as news articles, product descriptions, and social media posts.

Problems Solved:

• Addressing the challenge of generating high-quality natural language outputs in automated systems.
• Providing a method to train and augment language models for better performance in various applications.

Benefits:

• Improved natural language generation accuracy and coherence.
• Enhanced efficiency in generating large volumes of natural language content.
• Increased adaptability and context awareness in language models.

Commercial Applications:

• Optimizing customer service chatbots for better user interactions.
• Streamlining content creation processes for marketing and advertising agencies.
• Enhancing search engine optimization (SEO) strategies through natural language content generation.

Prior Art: Prior research in natural language processing and machine learning techniques for language model training and augmentation.

Frequently Updated Research: Stay updated on advancements in natural language generation systems, machine learning algorithms, and language model training techniques.

Questions about Natural Language Generation Systems: 1. How does the large language model improve the training process for natural language generation systems? 2. What are the potential limitations of augmenting small language models with external information from the large language model?

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.