17849056. RESPONDING TO TASK PROMPT ON DECLARATIVE CODE USING LANGUAGE MODEL simplified abstract (Microsoft Technology Licensing, LLC)

RESPONDING TO TASK PROMPT ON DECLARATIVE CODE USING LANGUAGE MODEL

Organization Name

Microsoft Technology Licensing, LLC

Inventor(s)

Benjamin Goth Zorn of Woodinville WA (US)

Carina Suzana Negreanu of Cambridge (GB)

Neil Blunt Toronto of Cambridge (GB)

Brian Paul Slininger of Seattle WA (US)

Andrew Donald Gordon of Cambridge (GB)

Advait Sarkar of Cambridge (GB)

Sruti Srinivasa Ragavan of Cambridge (GB)

RESPONDING TO TASK PROMPT ON DECLARATIVE CODE USING LANGUAGE MODEL - A simplified explanation of the abstract

This abstract first appeared for US patent application 17849056 titled 'RESPONDING TO TASK PROMPT ON DECLARATIVE CODE USING LANGUAGE MODEL

Simplified Explanation

The patent application describes a method for generating a response to a task prompt related to declarative code using a language model trained on imperative code. Here are the key points:

• The invention involves using a language model trained on imperative code to generate a response to a task prompt related to declarative code.
• Declarative code contains data declarations, while imperative code includes instructions on how to perform a task.
• The method involves converting a portion of the declarative code and task prompt into input imperative code.
• This input imperative code is then provided to the language model, which generates output imperative code.
• The generated output imperative code is then converted into a response to the task prompt.

Potential Applications

This technology has potential applications in various fields, including:

• Software development: It can assist developers in generating code snippets or providing guidance on how to perform tasks on declarative code.
• Data analysis: The method can be used to automate the generation of code for analyzing and manipulating declarative data.
• Natural language processing: The language model can be used to improve the understanding and generation of code from natural language instructions.

Problems Solved

The technology addresses the following problems:

• Bridging the gap between declarative and imperative code: It enables the generation of imperative code from declarative code and task prompts, facilitating the execution of tasks on declarative data.
• Code generation assistance: It provides developers with a tool to automatically generate code snippets or instructions based on declarative code and task prompts, saving time and effort.
• Improving code understanding: The language model helps in understanding and interpreting task prompts related to declarative code, leading to more accurate and relevant responses.

Benefits

The technology offers several benefits, including:

• Increased productivity: It automates the process of generating code, reducing the time and effort required for developers to perform tasks on declarative code.
• Enhanced code quality: The generated code is based on a language model trained on imperative code, which can improve the accuracy and reliability of the generated responses.
• Improved code comprehension: The method aids in understanding and interpreting task prompts related to declarative code, assisting developers in effectively working with declarative data.

Original Abstract Submitted

The generation of a response to a task prompt that represents a task to perform on declarative code. The response is generated with the aid of a language model that was trained on imperative code. The declarative code includes declarations about data. A task prompt represents a task to perform on the declarative code. At least a portion of the declarative code and at least a portion of the task prompt are converted into input imperative code. The input imperative code is then caused to be provided as input to the language model, resulting in the language model generating output imperative code. At least a portion of the output imperative code is then converted into a response to the task prompt.