Dynamic Document Annotation for Neural Network Training Labels
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Solution Overview
Problem
Manual labeling of training data for neural networks in Natural Language Processing and Large Language Models is time-consuming, costly, and prone to errors due to the vast amount of data required.
Innovation Solution
A system that dynamically correlates metadata to documents using large language models to generate training labels by identifying candidate locations and applying entailment scores, eliminating the need for manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to map metadata to document locations, then labeling accuracy can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an intermediary system comprising a query generator, neural network, and scoring mechanism that mediates between metadata and document locations. The query generator transforms metadata into natural language queries, the neural network retrieves candidate locations from the document, and entailment scores rank these candidates, thereby automating the manual labeling process while maintaining accuracy through multiple validation layers.
2Reliability
If manual labeling is performed to ensure quality training data, then labeling reliability improves, but error rate and cost increase
Solution Approach 1:
The system implements feedback through entailment scoring, where the neural network's candidate locations are evaluated against the original metadata queries. The entailment score provides a reliability metric that feedback loops can use to validate labeling quality, identify errors, and continuously improve the system's performance through retraining with high-confidence examples.
3Productivity
If automated labeling systems are implemented to reduce time and cost, then productivity increases, but complexity of the system increases
Solution Approach 1:
The patent segments the automated labeling system into distinct functional modules: a query generator that transforms metadata into natural language queries, a neural network that retrieves candidate locations, and an entailment scoring mechanism that ranks candidates. This segmentation allows each component to be independently optimized, maintained, and replaced, thereby managing system complexity while maintaining high productivity.
4Measurement precision
If extensive training data is collected for neural network training, then model performance improves, but data processing time and storage requirements increase
Solution Approach 1:
The system performs preliminary action by pre-processing metadata into structured natural language queries before neural network training. This preprocessing organizes the data into a format that maximizes training efficiency, allowing the neural network to learn from the semantic relationships in the queries rather than raw metadata, thereby improving model performance while reducing the effective data volume that needs to be processed.
Data Source
AI summary
A set of locations in a data object to be annotated is identified as corresponding to metadata of the data object. A natural language text query is generated using the metadata of a data object. A set of scores is generated for the set of locations using a generative neural network, and the set of scores indicate whether individual candidate locations satisfy the natural language text query. Based on the set of scores, a location in the data object is annotated to generate an annotated location as corresponding to the metadata.