Cascading LLM Prompt Processing for Accurate Command Generation
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Solution Overview
Problem
Conventional methods for generating commands for large language models (LLMs) often result in incorrect or incomplete outputs due to the complexity of natural language and variability in user inputs, particularly in complex scenarios requiring context-aware responses.
Innovation Solution
A cascading approach using multiple specialized instances of LLMs, each trained and prompted differently, to process complex prompts, where outputs from one instance are fed into subsequent instances to ensure accurate and complete command generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional prompting techniques are used for LLMs, then the system is simple and easy to operate, but the accuracy and completeness of command generation deteriorate
Solution Approach 1:
The patent divides a complex prompt into multiple sub-prompts and processes them through a cascade of specialized LLM instances. Each instance handles a specific aspect of the query (e.g., one for activities, one for vulnerabilities, one for controls), improving accuracy by focusing on specific tasks rather than attempting to handle all aspects in a single model.
Solution Approach 2:
The patent transitions from a single-dimensional prompting approach to a multi-dimensional cascading structure. Instead of one model processing the entire prompt, multiple models process different dimensions or aspects of the prompt sequentially, with each model's output feeding into the next, thereby enhancing overall command generation accuracy.
2Adaptability or versatility
If a single LLM processes complex prompts, then the system structure is simple, but the ability to handle context-aware responses and compound questions deteriorates
Solution Approach 1:
The patent segments compound questions into distinct sub-questions, each handled by a specialized LLM instance. For example, a query about activities, vulnerabilities, and controls is split into separate processing streams, with each stream managed by a model trained on specific aspects, thereby improving adaptability to complex queries.
Solution Approach 2:
The cascading LLM system creates a universal framework where multiple specialized models work together to handle diverse query types. Each model is trained on specific data (e.g., one on activities, one on vulnerabilities) but collectively they provide a multi-functional capability to address various aspects of complex prompts that a single model cannot handle effectively.
3Measurement precision
If conventional single-model prompting is used, then processing time is short, but the completeness and precision of outputs deteriorate
Solution Approach 1:
The patent segments the processing task across multiple specialized models, where each model quickly processes its specific aspect (e.g., identifying activities or vulnerabilities) and passes results to the next model. This segmented approach maintains precision by leveraging specialized knowledge in each model while managing processing time through parallelizable sub-tasks.
Solution Approach 2:
The cascading structure performs preliminary actions in sequence, where each model prepares and validates its output before passing it to the next model. This preliminary processing at each stage ensures precision is maintained throughout the cascade, with each model's output serving as refined input for the subsequent model, thereby achieving high precision without excessive time loss.
Data Source
AI summary
Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.