NLP Flow Arbitration Between LLM Accuracy and Response Latency
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Solution Overview
Problem
Existing natural language processing systems face challenges in efficiently handling nuanced requests due to the trade-off between latency and computational cost between LLM-based and non-LLM-based processing flows, with LLM-based flows being slower but better at processing complex requests, and non-LLM-based flows being faster but less nuanced.
Innovation Solution
Implementing an upstream processing flow arbitration component to select the appropriate natural language processing flow based on context and historical performance, and a downstream arbitration component to terminate or combine flows for optimal results, using machine learning and rule-based approaches to manage LLM-based and non-LLM-based processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLM-based processing flow is used, then request processing accuracy and nuance are improved, but processing latency and computational cost increase
Solution Approach 1:
The patent segments the natural language processing system into multiple processing flows (LLM-based and non-LLM-based) and uses an upstream arbitration component to route requests to appropriate flows. This segmentation allows the system to handle different request types through specialized pathways, reducing unnecessary computational overhead while maintaining high accuracy for complex requests.
Solution Approach 2:
The patent implements dynamic routing decisions where the upstream arbitration component selects processing flows based on request characteristics and historical performance data. The system dynamically adjusts which processing flows to use based on real-time conditions, optimizing the balance between accuracy and latency for each individual request.
2Measurement precision
If LLM-based processing flow is used, then request processing accuracy and nuance are improved, but computational cost increases
Solution Approach 1:
The patent divides the processing workload into segments by maintaining separate LLM-based and non-LLM-based processing flows. The arbitration component intelligently distributes requests to minimize computational resource consumption while ensuring accurate handling of nuanced requests through LLM-based processing only when necessary.
Solution Approach 2:
The system changes the parameter of processing flow selection based on request characteristics. By adjusting which processing flow is activated (LLM-based or non-LLM-based) based on factors like request complexity and historical performance, the system optimizes computational cost while maintaining processing accuracy.
3Speed
If non-LLM-based processing flow is used, then processing speed is improved, but request handling nuance and accuracy deteriorate
Solution Approach 1:
The patent segments processing capabilities into distinct LLM-based and non-LLM-based flows, allowing each to handle specific types of requests optimally. The upstream arbitration component routes simple requests to faster non-LLM-based processing while directing complex nuanced requests to LLM-based processing, thus maintaining both speed and accuracy.
Solution Approach 2:
The system dynamically selects processing flows based on request characteristics. By making real-time decisions about which processing path to take based on the nature of the request and historical performance data, the system optimizes both processing speed and accuracy for different request types.
4Adaptability or versatility
If multiple processing flows are implemented, then system adaptability and request handling capability are improved, but system complexity increases
Solution Approach 1:
The patent introduces an upstream arbitration component as an intermediary that manages the complexity of multiple processing flows. This mediator component handles the routing decisions, selecting appropriate processing flows based on request characteristics and historical performance, thereby enabling system adaptability while containing complexity through a dedicated management layer.
Solution Approach 2:
The arbitration component serves multiple functions: it routes requests to appropriate processing flows, monitors historical performance, and makes adaptive decisions based on various factors. This multi-functionality reduces the need for separate management mechanisms, thereby managing system complexity while maintaining high adaptability.
Data Source
AI summary
Devices and techniques are generally described for arbitration between LLM-based and intent-based natural language processing flows. In various examples, an automatic speech recognition (ASR) component may generate first ASR output data representing the first natural language input. A first machine learning model may select an LLM-based natural language processing flow. The first machine learning model may be trained to select between at least the LLM-based and non-LLM-based natural language processing flows. The first ASR output data may be processed using the LLM-based processing flow. The LLM-based processing flow may generate first executable data.