Salesforce, inc. (20240289606). SYSTEMS AND METHODS FOR AN ENCODER-DECODER BASED FRAMEWORK FOR CODE GENERATION AND UNDERSTANDING simplified abstract

SYSTEMS AND METHODS FOR AN ENCODER-DECODER BASED FRAMEWORK FOR CODE GENERATION AND UNDERSTANDING

Organization Name

salesforce, inc.

Inventor(s)

Yue Wang of Singapore (SG)

Hung Le of Singapore (SG)

Akhilesh Deepak Gotmare of Singapore (SG)

Junnan Li of Singapore (SG)

Chu Hong Hoi of Singapore (SG)

SYSTEMS AND METHODS FOR AN ENCODER-DECODER BASED FRAMEWORK FOR CODE GENERATION AND UNDERSTANDING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240289606 titled 'SYSTEMS AND METHODS FOR AN ENCODER-DECODER BASED FRAMEWORK FOR CODE GENERATION AND UNDERSTANDING

Simplified Explanation

The patent application describes a framework that combines encoder-decoder transformer technology for multi-task pretraining and flexible finetuning for code understanding and generation tasks.

• The framework includes multimodal encoder and decoder modules.
• It is trained with multiple learning objectives during pre-training, including self-supervised tasks on unimodal and bimodal data.

Key Features and Innovation

• Mixture of encoder-decoder transformer framework
• Multi-task pretraining and flexible finetuning
• Multimodal encoder and decoder modules
• Training with multiple learning objectives

Potential Applications

This technology can be applied in various fields such as natural language processing, machine translation, and code generation tasks.

Problems Solved

The framework addresses the need for efficient pretraining and finetuning methods for code understanding and generation tasks.

Benefits

• Improved performance in code understanding and generation tasks
• Enhanced flexibility in model training and adaptation

Commercial Applications

• Natural language processing software development
• Machine translation tools
• Code generation platforms

Questions about the Technology

How does this framework improve upon existing methods for code understanding and generation?

This framework enhances performance by combining encoder-decoder transformer technology with multimodal modules for efficient pretraining and finetuning.

What potential impact could this technology have on the field of natural language processing?

This technology could lead to advancements in natural language processing tasks by improving model training and adaptation processes.

Original Abstract Submitted

embodiments described herein provide a mixture of encoder-decoder transformer framework for multi-task pretraining and flexible finetuning for both code understanding and generation tasks. specifically, the framework is built on multimodal encoder and decoder modules. during pre-training, the encoder-decoder framework is trained with multiple learning objectives, including a diverse set of self-supervised tasks over two major stages of pretraining on unimodal and bimodal data.