Language Model Input Context Enrichment for Accurate Domain Predictions
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Solution Overview
Problem
Existing NLP-based technologies lack contextual understanding and consume excessive computing resources due to large model sizes and inefficient training processes, leading to inaccurate predictions and high computational overhead.
Innovation Solution
Incorporating a corpus of text and a corpus data supplement, such as metadata, as input into a machine learning model to enhance contextual understanding and reduce computational requirements by eliminating the need for fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual metadata is provided as corpus data supplement, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The input data is segmented into distinct components: the original corpus text and the corpus data supplement (contextual metadata). This segmentation allows the model to process structured information separately from unstructured text, improving prediction accuracy while maintaining manageable complexity through organized data flow.
Solution Approach 2:
The corpus data supplement acts as an intermediary layer between the original corpus and the language model. This intermediary provides contextual metadata that bridges the gap between raw text and model interpretation, enhancing accuracy without requiring direct modification of the model architecture.
2Measurement precision
If extensive fine-tuning is performed to improve model understanding, then prediction accuracy is improved, but loss of time increases
Solution Approach 1:
Contextual metadata is prepared and attached to the corpus in advance, before model inference. This preliminary organization of information allows the model to make accurate predictions without requiring extensive fine-tuning, significantly reducing training time while maintaining high prediction accuracy.
3Measurement precision
If larger model size is used to improve understanding capability, then prediction accuracy is improved, but use of energy increases
Solution Approach 1:
Instead of using a larger model to process all information uniformly, the system applies local quality enhancement by adding contextual metadata specifically where needed in the corpus. This targeted approach improves prediction accuracy for specific domains without requiring increased overall model size, thereby reducing computational resource consumption.
4Measurement precision
If more contextual data is provided, then prediction accuracy is improved, but quantity of substance increases
Solution Approach 1:
The system extracts only the most relevant contextual metadata from available information and attaches it as a supplement to the corpus. This selective extraction provides sufficient context for accurate predictions without unnecessarily increasing input data volume, maintaining efficiency while improving accuracy.
Data Source
AI summary
Various embodiments discussed herein are directed to improving existing technologies by providing a corpus data supplement as input into a model, such as a Large Language Model (LLM). Consequently, the model can generate accurate scores or data for predictions because the model is better able to distinguish between a general understanding of natural language concepts and domain-specific concepts.