Multi-Stage Prompting for Accurate Target Classification Signatures
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Solution Overview
Problem
Existing machine learning techniques struggle with accurately extracting predictive entities from textual data for target classifications due to resource-intensive manual iterations and inaccuracies from large language models, particularly due to hallucinations and a mix of relevant and irrelevant entities.
Innovation Solution
A multi-stage prompting pipeline that includes a machine learning classifier for detecting predictive text segments, followed by staged prompting with a large language model to iteratively extract details, and finally clustering and summarizing these segments to generate optimal target classification signatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual iteration methods are used with subject matter experts to extract predictive entities from textual data, then the accuracy of entity extraction is improved, but the resource consumption and time required increase significantly
Solution Approach 1:
The patent segments the entity extraction process into multiple stages: initial entity extraction, predictive significance scoring, and iterative refinement. Each stage processes a subset of entities with different levels of scrutiny, allowing automated systems to handle high-volume initial extraction while human experts focus only on ambiguous cases that require manual review.
Solution Approach 2:
The system implements feedback loops where extraction results are continuously evaluated and used to refine the extraction model. Predictive significance scores are updated based on performance feedback, and the system iteratively improves by learning from both automated results and manual expert corrections, progressively enhancing accuracy while maintaining efficiency.
2Extent of automation
If large language models are used to extract entities from textual data, then the automation level is improved, but the accuracy deteriorates due to hallucinations and irrelevant entity extraction
Solution Approach 1:
The patent introduces an intermediary validation layer between the large language model and final entity extraction. This intermediary component includes predictive significance scoring mechanisms and confidence threshold filtering that verify LLM outputs before acceptance, catching hallucinations and irrelevant extractions while preserving the automation benefits of the LLM.
Solution Approach 2:
The system dynamically adjusts extraction parameters such as confidence thresholds, entity type filters, and predictive significance cutoffs based on the specific textual context and classification task. This allows the automated system to adapt its sensitivity and selectivity, improving accuracy by tuning parameters rather than relying on fixed LLM behavior.
3Device complexity
If a single-stage extraction approach is used, then the process simplicity is improved, but the ability to handle diverse text types and classification needs deteriorates
Solution Approach 1:
The patent implements a dynamic multi-stage extraction pipeline where the number and type of processing stages can be adjusted based on the specific classification task and text characteristics. The system can operate in simplified mode for straightforward cases or expand to full multi-stage processing for complex classifications, providing adaptability without requiring completely different systems for different tasks.
Data Source
AI summary
Various embodiments of the present disclosure provide machine learning architectures and data processing techniques for improving computer-based text comprehension. The techniques include generating, using a trained classifier model, target classification probabilities for labelled text-based objects from a testing portion of a labelled training dataset and identifying predictive text-based objects from the labelled text-based objects based on the target classification probabilities. The techniques include applying a staged prompting mechanism with a generative extraction model to identify a target set of explanatory text segments from the predictive text-based objects that may be clustered into semantic segment clusters. The techniques include generating explanatory summary segments respectively corresponding to the semantic segment clusters and generating a target classification signature based on a plurality of terms from the one or more explanatory summary segments.