Inference Pattern Engine Training Through LLM Response Mapping
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Solution Overview
Problem
Large language models (LLMs) are prone to hallucinations and are computationally expensive, leading to inconsistent outputs and inefficiencies with larger input sizes, which hampers their scalability and speed in generating responses.
Innovation Solution
An inference pattern engine is used to map variables in inputs to corresponding elements in webpages or applications, utilizing beacon nodes and stored patterns to generate responses without relying on LLMs, reducing hallucinations and response times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used to perform functional tasks, then they can generate responses, but they are prone to hallucinations and produce inconsistent outputs
Solution Approach 1:
The patent creates simplified copies of LLM responses by extracting only the essential variable-to-element mappings. Instead of relying on the full LLM generation process, the system copies the functional output (the mapping relationships) and stores them as reusable patterns, eliminating hallucinations while preserving the core functionality.
Solution Approach 2:
The system performs preliminary action by pre-processing LLM responses to extract and store inference patterns before they are needed. These patterns are cached and reused for subsequent queries, avoiding the need to run expensive LLM inference repeatedly and ensuring consistent outputs.
2Adaptability or versatility
If LLMs are used to generate responses, then functional tasks can be performed, but they are computationally expensive and slow
Solution Approach 1:
The system performs the computationally expensive LLM inference in advance, extracts the essential patterns, and stores them for rapid retrieval. This preliminary action separates the heavy computation from the fast response generation, dramatically improving productivity.
Solution Approach 2:
Instead of repeatedly executing the full LLM process, the system creates lightweight copies of the inference logic in the form of stored patterns that can be rapidly applied to new inputs without requiring the original computational resources.
3Quantity of substance
If LLM context window size increases to handle larger inputs, then more information can be processed, but response generation time increases
Solution Approach 1:
The patent extracts only the essential variable-to-element mapping information from the full LLM response, separating the critical functional data from the redundant reasoning steps. This extraction reduces the amount of data that needs to be processed and stored, maintaining input processing capacity while reducing time costs.
Solution Approach 2:
The system segments the LLM response into discrete inference patterns that can be independently stored and retrieved. This segmentation allows the system to handle large inputs by breaking them into manageable pattern units, processing only what is necessary for each specific query.
Data Source
AI summary
A prompt is generated based on an input and processed webpage or application content. The prompt is provided to a large language model. Based on a response received from the large language model that maps one or more variables associated with the input to one or more elements associated with the processed content associated with the tree data structure received from the large language, a pattern mapping the one or more variables associated with the input to the one or more elements associated with the processed content associated with the tree data structure is derived.