18431180. Token Packing for Sequence Models simplified abstract (Microsoft Technology Licensing, LLC)

Token Packing for Sequence Models

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Andy Wagner of Cupertino CA (US)

Tiyasa Mitra of San Jose CA (US)

Marc Tremblay of Bellevue WA (US)

Token Packing for Sequence Models - A simplified explanation of the abstract

This abstract first appeared for US patent application 18431180 titled 'Token Packing for Sequence Models

Simplified Explanation

Embodiments of the present disclosure include systems and methods for packing tokens to train sequence models. In some embodiments, a plurality of datasets for training a sequence model is received. Each dataset in the plurality of datasets includes a sequence of correlated tokens. A set of training data is generated that includes a subset of a sequence of tokens from a first dataset in the plurality of datasets and a subset of a sequence of tokens from a second, different dataset in the plurality of datasets. The sequence model is trained using the set of training data.

• Systems and methods for packing tokens to train sequence models
• Receive multiple datasets for training a sequence model, each containing a sequence of correlated tokens
• Generate a set of training data by combining subsets of tokens from different datasets
• Train the sequence model using the set of training data

Potential Applications

This technology could be applied in:

• Natural language processing
• Speech recognition systems
• Machine translation algorithms

Problems Solved

This technology helps to:

• Improve the accuracy of sequence models
• Enhance the performance of machine learning algorithms
• Optimize training processes for large datasets

Benefits

The benefits of this technology include:

• Increased efficiency in training sequence models
• Enhanced predictive capabilities in various applications
• Better utilization of correlated token data for training

Potential Commercial Applications

This technology has potential commercial applications in:

• Software development companies
• Data analytics firms
• Artificial intelligence research organizations

Possible Prior Art

One possible prior art for this technology could be:

• Existing methods for training sequence models using token data

Unanswered Questions

How does this technology compare to existing token packing methods in terms of efficiency and accuracy?

This article does not provide a direct comparison with existing token packing methods to evaluate efficiency and accuracy.

Are there any limitations or constraints in the implementation of this technology in real-world applications?

The article does not address any potential limitations or constraints that may arise when implementing this technology in real-world scenarios.

Original Abstract Submitted

Embodiments of the present disclosure include systems and methods for packing tokens to train sequence models. In some embodiments, a plurality of datasets for training a sequence model is received. Each dataset in the plurality of datasets includes a sequence of correlated tokens. A set of training data is generated that includes a subset of a sequence of tokens from a first dataset in the plurality of datasets and a subset of a sequence of tokens from a second, different dataset in the plurality of datasets. The sequence model is trained using the set of training data.