View source for 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==

[[:Category:salesforce, inc.|salesforce, inc.]]


[[Category:salesforce, inc.]]

==Inventor(s)==

[[:Category:Yue Wang of Singapore (SG)|Yue Wang of Singapore (SG)]][[Category:Yue Wang of Singapore (SG)]]

[[:Category:Hung Le of Singapore (SG)|Hung Le of Singapore (SG)]][[Category:Hung Le of Singapore (SG)]]

[[:Category:Akhilesh Deepak Gotmare of Singapore (SG)|Akhilesh Deepak Gotmare of Singapore (SG)]][[Category:Akhilesh Deepak Gotmare of Singapore (SG)]]

[[:Category:Junnan Li of Singapore (SG)|Junnan Li of Singapore (SG)]][[Category:Junnan Li of Singapore (SG)]]

[[:Category:Chu Hong Hoi of Singapore (SG)|Chu Hong Hoi of Singapore (SG)]][[Category: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.



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