Deepmind technologies limited (20240184982). HIERARCHICAL TEXT GENERATION USING LANGUAGE MODEL NEURAL NETWORKS simplified abstract

HIERARCHICAL TEXT GENERATION USING LANGUAGE MODEL NEURAL NETWORKS

Organization Name

deepmind technologies limited

Inventor(s)

Kory Wallace Mathewson of Montreal (CA)

Piotr Wojciech Mirowski of London (GB)

Richard Andrew Evans of London (GB)

HIERARCHICAL TEXT GENERATION USING LANGUAGE MODEL NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240184982 titled 'HIERARCHICAL TEXT GENERATION USING LANGUAGE MODEL NEURAL NETWORKS

Simplified Explanation

The patent application describes methods, systems, and apparatus for generating long textual works using language model neural networks. This involves performing a hierarchy of generation steps using the same neural network to create textual works.

• Language model neural networks are used to generate long textual works.
• The textual works are created hierarchically through a series of generation steps.
• The same neural network is utilized for all generation steps.

Potential Applications:

• Content generation for websites, blogs, and social media platforms.
• Automated writing for news articles, reports, and essays.
• Personalized content creation for marketing campaigns and product descriptions.

Problems Solved:

• Streamlining the process of generating long textual works.
• Improving efficiency and accuracy in content creation.
• Reducing the time and effort required for writing tasks.

Benefits:

• Faster production of high-quality written content.
• Consistent tone and style throughout the generated text.
• Increased productivity for writers and content creators.

Commercial Applications:

• Content marketing agencies can use this technology to produce large volumes of content quickly.
• E-commerce platforms can automate product descriptions and reviews.
• News organizations can generate news articles in real-time.

Prior Art: There may be existing technologies related to language model neural networks for text generation, but specific details are not provided in the abstract.

Frequently Updated Research: Stay updated on advancements in language model neural networks and their applications in text generation for the latest developments in this field.

Questions about Text Generation using Language Model Neural Networks:

Question 1: How can language model neural networks improve the efficiency of content creation? Answer: Language model neural networks can streamline the process by generating text hierarchically, reducing the time and effort required for writing tasks.

Question 2: What are the potential challenges in implementing language model neural networks for text generation? Answer: Challenges may include fine-tuning the neural network for specific writing styles and ensuring the generated text is coherent and accurate.

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating long textual works using language model neural networks. for example, the textual works can be generated hierarchically by performing a hierarchy of generation steps using the same language model neural network.