Google llc (20240211752). 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 20240211752 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, and text generation tasks.

Problems Solved

• Efficient generation of output sequences from input sequences.
• Improved accuracy in predicting the next output token.

Benefits

• Enhanced performance in sequence generation tasks.
• Increased efficiency in processing input sequences.

Commercial Applications

The technology can be utilized in chatbots, language translation services, and content generation tools, enhancing their accuracy and efficiency.

Prior Art

Readers can explore prior research on self-attention mechanisms in neural networks and sequence generation techniques.

Frequently Updated Research

Stay updated on advancements in self-attention mechanisms in neural networks and their applications in sequence generation tasks.

Questions about the Technology

How does the self-attention decoder neural network improve sequence generation?

The self-attention decoder neural network enhances sequence generation by considering the entire input sequence when predicting the next output token.

What are the potential limitations of using self-attention mechanisms in sequence generation?

While self-attention mechanisms offer benefits in sequence generation tasks, they may require significant computational resources and training data.

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.