Assistant LLM Entry Points for Fast Function Switching
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Solution Overview
Problem
Existing large language models (LLMs) lack flexibility in handling multiple functionalities and require cumbersome switching between different assistants or constructing elaborate prompts, leading to inefficiencies in user interaction.
Innovation Solution
An assistant LLM that uses entry points, such as hotwords or UI elements, to receive trigger inputs, obtain adaptation inputs, and seamlessly adapt to specified functionalities, enabling efficient interaction with external LLMs and reducing user latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users switch between different LLM assistants to access different functionalities, then they can access specialized capabilities, but the switching process is cumbersome and increases interaction time
Solution Approach 1:
The patent combines multiple LLM assistants into a single unified assistant that can dynamically adapt its behavior. Instead of requiring users to switch between separate assistant applications, the unified assistant integrates multiple specialized LLMs and switches between them based on the detected functionality requirements in user prompts, thereby eliminating switching time while maintaining access to diverse capabilities
Solution Approach 2:
The assistant implements dynamic functionality adaptation by analyzing user prompts in real-time and adjusting its behavior accordingly. The system dynamically determines which specialized LLM to engage based on the detected functionality type, allowing the assistant to transform from a static single-function model to a dynamic multi-function system that adapts to user needs without requiring manual switching
2Measurement precision
If users construct elaborate prompts to specify functionality, then they can achieve precise control over assistant behavior, but the interaction complexity increases and efficiency decreases
Solution Approach 1:
The assistant implements self-service functionality detection by automatically analyzing user prompts to identify the intended functionality type. Instead of requiring users to manually specify functionality through elaborate prompts, the system autonomously detects the functionality requirements and selects the appropriate specialized LLM, thereby maintaining precise control while dramatically simplifying user interaction
Solution Approach 2:
The patent replaces the mechanical approach of manual functionality specification with an automated detection mechanism. The system uses natural language processing to automatically interpret user intent and determine the required functionality, substituting the complex mechanical process of constructing elaborate prompts with an intelligent automated detection system that achieves the same precision with much simpler user input
3Device complexity
If a single LLM is trained to handle multiple functionalities, then the system structure is simplified, but the model size and training complexity increase significantly
Solution Approach 1:
The patent segments the multi-functionality requirement into separate specialized LLMs, each trained for a specific functionality type. Instead of creating one massive model attempting to handle all functionalities, the system divides the workload across multiple smaller, specialized models, reducing the parameter count per model while maintaining overall system capability through coordinated deployment of these segmented components
Data Source
AI summary
A method includes receiving a particular trigger input directed toward an assistant large language model (LLM). The particular trigger input specifying a particular functionality for the assistant LLM to undertake for processing a follow-on query from the user. The method also includes obtaining an adaptation input specifically formulated for adapting the assistant LLM to undertake the particular functionality specified by the particular trigger input. The method also includes receiving the follow-on query and providing the adaptation input for input to the assistant LLM. The method also includes processing the follow-on query to fulfill performance of an action specified by the natural language query using the adapted assistant LLM undertaking the particular functionality specified by the particular trigger input.