Task-Centric Code Utterances for Editable Program Generation
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Solution Overview
Problem
Conventional large language models struggle with effectively interpreting and generating responses to natural language inputs that are not linear or do not align with their training data, leading to inefficiencies and user frustration.
Innovation Solution
The automated generation of a task-centric representation of code, accompanied by natural language explanations, allows users to edit and modify the code through natural language interactions, aligning with the language model's understanding and facilitating effective code modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional large language models are trained on linear sequential text data, then they can generate linear sequence output text, but they struggle to effectively interpret and generate responses to non-linear natural language inputs
Solution Approach 1:
The patent segments code into multiple tasks with a task-centric representation, where each task is independently identified and can be processed separately. This segmentation allows the system to handle complex code generation by breaking it down into manageable units that can be effectively interpreted and modified.
Solution Approach 2:
The patent introduces an intermediary layer between natural language input and code output - a task-centric representation that includes task identifiers, task names, and task descriptions. This intermediary structure bridges the gap between linear language models and non-linear code requirements, enabling effective interpretation and generation.
2Ease of operation
If users directly interact with code, then code can be precisely modified, but user familiarity with the language model's language space is reduced
Solution Approach 1:
The task-centric representation serves as an intermediary between user intent and code modification. Users interact with natural language descriptions of tasks rather than directly editing code, making operations easier while the system maintains alignment with the language model's understanding through the structured task representation.
Solution Approach 2:
The system changes the parameter space from direct code editing to task-level natural language descriptions. By operating at the task description level rather than code syntax level, users can more easily express intent while the system translates these high-level descriptions into precise code modifications.
3Reliability
If natural language expressions are used to generate code, then code generation alignment with user intent can be improved, but some expressions may result in no output or ineffective interpretation
Solution Approach 1:
The system performs preliminary action by generating a task-centric representation before actual code generation. This preliminary structuring of user intent into identifiable tasks with clear descriptions enables the language model to more reliably interpret requirements and generate appropriate code, reducing failed generation attempts.
Solution Approach 2:
The task-centric representation provides a feedback mechanism where the structured breakdown of tasks can be reviewed and refined before code generation. This allows for verification that user intent is properly captured and can guide the language model to generate more aligned code output.
Data Source
AI summary
The automated generation of a natural language explanation of what code does. The code is structured to perform tasks because the code itself semantically specifies that those tasks are to be performed. A task-centric representation of the code is automatically generated that includes a task representation of each of some or all of the tasks to be performed as specified by the code. Natural language utterances are then automatically generated by generating a corresponding natural language utterance that semantically describes in natural language the corresponding task represented by the corresponding task representation. Controls are rendered for each natural language utterance that each permit a user to edit the corresponding natural language utterance. After editing, the code itself may be automatically modified or regenerated to reflect the changed natural language utterances.