Google llc (20240160857). Contrastive Pre-Training for Language Tasks simplified abstract

Contrastive Pre-Training for Language Tasks

Organization Name

google llc

Inventor(s)

Thang Minh Luong of Santa Clara CA (US)

Quoc V. Le of Sunnyvale CA (US)

Kevin Stefan Clark of San Francisco CA (US)

Contrastive Pre-Training for Language Tasks - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240160857 titled 'Contrastive Pre-Training for Language Tasks

Simplified Explanation

The patent application describes a method for training a machine-learned language encoding model using a contrastive learning task. This task involves the encoder learning to distinguish input tokens from plausible alternatives by masking out some tokens, replacing them with samples from a generator, and training the encoder to predict whether each token is original or a replacement.

• The method involves training a machine-learned language encoding model through a contrastive learning task.
• The encoder learns to distinguish input tokens from plausible alternatives by masking out some tokens and replacing them with samples from a generator.
• The encoder is trained to predict whether each token is original or a replacement produced by the generator.

Potential Applications

This technology could be applied in natural language processing tasks such as text generation, machine translation, and sentiment analysis.

Problems Solved

This technology helps improve the performance of language encoding models by training them to distinguish between original tokens and replacements, leading to better language understanding and generation.

Benefits

The benefits of this technology include enhanced language encoding model performance, improved text generation accuracy, and better natural language processing capabilities.

Potential Commercial Applications

This technology could be used in various commercial applications such as chatbots, virtual assistants, content generation tools, and automated customer support systems.

Possible Prior Art

One possible prior art for this technology could be contrastive learning methods used in computer vision tasks to improve image recognition and classification accuracy.

What are the limitations of this technology in real-world applications?

This technology may require a large amount of training data to achieve optimal performance in real-world applications. Additionally, the effectiveness of the contrastive learning task may vary depending on the specific language encoding model and dataset used.

How does this technology compare to existing language encoding models in terms of performance and efficiency?

This technology aims to improve the performance of language encoding models by training them through a contrastive learning task, which may lead to better accuracy and efficiency compared to traditional training methods. However, further comparative studies and benchmarks are needed to evaluate its effectiveness against existing models.

Original Abstract Submitted

systems and methods are provided that train a machine-learned language encoding model through the use of a contrastive learning task. in particular, the present disclosure describes a contrastive learning task where the encoder learns to distinguish input tokens from plausible alternatives. in some implementations, on each training example the proposed method masks out some subset (e.g., 15%) of the original input tokens, replaces the masked tokens with samples from a “generator” (e.g., which may be a small masked language model), and then trains the encoder to predict whether each token comes from the original data or is a replacement produced by the generator.