Natural Language Task Decomposition for Component Routing
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Solution Overview
Problem
Existing natural language processing systems struggle to efficiently process complex user inputs, prioritize tasks, and select appropriate components to perform actions in a logical order, leading to inefficiencies and inaccuracies in task completion.
Innovation Solution
A system utilizing large language models (LLMs) to determine tasks, prioritize them, and select relevant components to perform actions based on user inputs, incorporating personalized information and context to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing natural language processing systems process complex user inputs using traditional methods, then the system structure remains simple, but the task completion accuracy and efficiency deteriorate
Solution Approach 1:
The system segments complex user inputs into multiple prioritized tasks and assigns them to different specialized components (NLU component, planning component, skill component, API component) for processing. This segmentation allows each component to focus on specific aspects of task completion, improving overall accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system introduces a temporal dimension by prioritizing tasks in a specific order and processing them sequentially through different components. This adds a time-based dimension to the processing architecture, allowing complex inputs to be broken down and handled in logical sequences, thereby improving task completion accuracy.
2Productivity
If existing systems process all user input tasks simultaneously, then the processing is comprehensive, but the processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by prioritizing tasks and identifying the most critical tasks first before processing them. The planning component generates a prioritized task list, and the system processes high-priority tasks through the NLU and skill components first, thereby improving processing efficiency and reducing overall task completion time.
Solution Approach 2:
The system dynamically adjusts the processing order and resource allocation based on task priority levels. High-priority tasks receive immediate attention and are processed through the full component chain, while lower-priority tasks can be processed in parallel or deferred, optimizing both efficiency and time management.
3Manufacturing precision
If existing systems use generic processing methods for all user inputs, then the system is easy to operate, but the accuracy in selecting appropriate components deteriorates
Solution Approach 1:
The system applies local quality by using different processing strategies and component selections tailored to specific task types and priority levels. The NLU component analyzes input to determine task characteristics, and based on this analysis, the system selectively engages appropriate components (planning component for complex tasks, skill component for straightforward tasks, API component for data retrieval), thereby improving component selection accuracy while maintaining operational simplicity through automatic routing.
Data Source
AI summary
Techniques for generating tasks to be completed in order to perform an action responsive to a user input and, for a given task, shortlisting available components to those that are relevant for the task are described. The system processes a user input to determine tasks to be completed in order to perform an action responsive to the user input. The system determines a priority of the tasks and selects a top-ranked task. The system determines descriptions of processing performable by components that are semantically similar to the current task, and requests a description of the function the corresponding components would perform for the current task. Based on the received descriptions, the system selects one or more components to perform the task. Thereafter, the system causes the action to be performed and outputs a response to the user input.