Natural Language Processing with LLM Response Arbitration
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Solution Overview
Problem
Existing natural language processing systems are limited in their ability to effectively integrate and combine responses from multiple types of components, such as APIs and LLM-based agents, leading to suboptimal user interactions and potential ambiguities in processing user inputs.
Innovation Solution
A system that utilizes an LLM orchestrator to manage and integrate responses from various components like APIs, skill components, and LLM agents, employing a response arbitration component to select and combine relevant responses, and perform actions based on user inputs, while ensuring compliance with user permissions and regulatory standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple types of components (APIs, LLM-based agents, skill components) are integrated to process user inputs, then the comprehensiveness and accuracy of responses is improved, but the system complexity increases
Solution Approach 1:
The system is divided into distinct component types (APIs, LLM-based agents, skill components) that each handle specific aspects of user input processing. The orchestrator component further segments the coordination function, assigning different components to different tasks based on their specialized capabilities, thereby managing complexity through functional segmentation while maintaining high response accuracy.
Solution Approach 2:
The orchestrator component serves as an intermediary that manages the integration between multiple component types. It receives user inputs, determines which components should process them, coordinates their responses, and synthesizes final outputs. This intermediary layer simplifies the overall system architecture by centralizing the coordination logic while enabling comprehensive multi-component integration.
2Reliability
If multiple components process user inputs simultaneously, then the comprehensiveness of responses is improved, but the processing time increases
Solution Approach 1:
The orchestrator dynamically determines which components to activate based on the specific user input and task requirements. Rather than always invoking all components simultaneously, the system adaptively selects and activates only the necessary components for each particular request, thereby maintaining comprehensive response quality while reducing unnecessary processing time for simpler tasks.
Solution Approach 2:
The system employs partial action by activating only the subset of components needed for each specific user input. The orchestrator assesses the input and invokes only those components whose capabilities are relevant to the task at hand, avoiding the time cost of engaging all components for every request while still ensuring comprehensive processing when necessary.
3Ease of operation
If responses from multiple components are integrated and combined, then the quality of user interactions is improved, but the difficulty of managing and coordinating components increases
Solution Approach 1:
The orchestrator component is designed with universal functionality to handle diverse component types (APIs, LLM-based agents, skill components) through a unified interface and coordination mechanism. It performs multiple functions including task assignment, response aggregation, and output synthesis, thereby simplifying the management of heterogeneous components while maintaining high-quality user interactions.
Solution Approach 2:
The orchestrator acts as an intermediary that simplifies the coordination between multiple component types. It provides a standardized interface for task assignment and response collection, managing the complexity of inter-component communication while enabling seamless integration. This intermediary layer shields users from the underlying coordination complexity, delivering high-quality interactions.
Data Source
AI summary
Techniques for determining one or more responses associated with one or more components that are responsive to a user input are described. The system receives a user input and causes one or more components to generate one or more responses associated with the user input. The system determines one or more of the responses are responsive to the user input, causes one or more actions associated with the responses to be performed, and outputs a natural language summary of the one or more responses. If the system determines that none of the responses are responsive to the user input and/or an ambiguity exists with respect to the user input, the system can generate a request for additional information usable to resolve the ambiguity, which may be sent to another component of the system and/or output to the user that provided the user input.