Dual ML Model Context Selection for Document Inferencing
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Solution Overview
Problem
Large language models like GPT-3 face challenges in scalability and resource efficiency due to their high computational complexity, which limits their deployment and response time, and they often provide generic answers without sufficient context.
Innovation Solution
The system employs a dual-machine learning model approach, where a first ML model selects relevant information sources based on context relevance, and a second ML model transforms the input prompt into a human-readable response by applying its parameters to the selected context information, thereby enhancing response specificity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to improve response quality, then response accuracy is improved, but computational complexity and resource consumption increase
Solution Approach 1:
The system segments the response generation task into two parts: a first ML model selects relevant information sources based on the input prompt, and a second ML model generates the final response using selected context. This segmentation allows smaller, more efficient models to be used while maintaining response quality.
Solution Approach 2:
The first ML model acts as an intermediary that selects and filters information sources before passing them to the second ML model. This intermediary step reduces the computational burden on the response generation model by providing pre-filtered, relevant context.
2Measurement precision
If large language models are used to improve response quality, then response accuracy is improved, but response time increases
Solution Approach 1:
The task is divided into information source selection and response generation, allowing parallel processing and reducing overall response time while maintaining accuracy through specialized model functions.
Solution Approach 2:
The first ML model performs preliminary action by selecting and preparing relevant information sources before the second model generates the response. This preliminary filtering accelerates the overall process by reducing the processing burden on the response generation model.
3Measurement precision
If context information is added to improve response specificity, then response accuracy is improved, but device complexity increases
Solution Approach 1:
The first ML model serves as an intermediary that automatically selects and manages context information, reducing the complexity burden on the response generation model while still providing necessary context for accurate responses.
Solution Approach 2:
The system copies only the most relevant information sources selected by the first ML model to the second model, rather than providing all available context. This selective copying reduces system complexity while maintaining response accuracy.
Data Source
AI summary
A method for assisted context generation for enhanced accuracy in document inferencing includes designating, by a device including a processor and in response to an input prompt, a selected context information source from a group of context information sources using a first machine learning model. The selected context information source is designated based on relevance of the selected context information source to the input prompt. The method further includes constructing, by the device and using a second machine learning model that is not the first machine learning model, a human-readable response to the input prompt by applying parameters of the second machine learning model to the input prompt and the selected context information source.