Google llc (20240256859). ATTENTION-BASED DECODER-ONLY SEQUENCE TRANSDUCTION NEURAL NETWORKS simplified abstract

ATTENTION-BASED DECODER-ONLY SEQUENCE TRANSDUCTION NEURAL NETWORKS

Organization Name

google llc

Inventor(s)

Noam M. Shazeer of Palo Alto CA (US)

Lukasz Mieczyslaw Kaiser of San Francisco CA (US)

Etienne Pot of Palo Alto CA (US)

Mohammad Saleh of Santa Clara CA (US)

Ben David Goodrich of San Francisco CA (US)

Peter J. Liu of Santa Clara CA (US)

Ryan Sepassi of Beverly Hills CA (US)

ATTENTION-BASED DECODER-ONLY SEQUENCE TRANSDUCTION NEURAL NETWORKS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240256859 titled 'ATTENTION-BASED DECODER-ONLY SEQUENCE TRANSDUCTION NEURAL NETWORKS

Simplified Explanation: The patent application describes methods, systems, and apparatus for generating an output sequence from an input sequence using a self-attention decoder neural network.

Key Features and Innovation:

• Generation of an output sequence from an input sequence at multiple time steps.
• Utilization of a self-attention decoder neural network to process the combined sequence.
• Selection of the next output token based on the score distribution over possible output tokens.

Potential Applications: This technology can be applied in natural language processing, machine translation, speech recognition, and other sequence generation tasks.

Problems Solved: This technology addresses the need for efficient and accurate sequence generation from input data in various applications.

Benefits:

• Improved accuracy in generating output sequences.
• Enhanced efficiency in processing input sequences.
• Versatile application across different domains requiring sequence generation.

Commercial Applications: The technology can be utilized in chatbots, language translation services, content generation tools, and data analysis systems, potentially impacting the market for AI-driven solutions.

Prior Art: Readers can explore prior research on self-attention mechanisms in neural networks, sequence-to-sequence models, and transformer architectures to understand the background of this technology.

Frequently Updated Research: Stay updated on advancements in self-attention mechanisms, neural network architectures for sequence generation, and applications of transformer models in various fields.

Questions about the Technology: 1. How does the self-attention decoder neural network improve sequence generation compared to traditional methods? 2. What are the potential limitations or challenges in implementing this technology in real-world applications?

Original Abstract Submitted

methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. one of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.