Natural Language Processing Pipelines Using Bounded-Scope Determinism
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current natural language processing (NLP) technologies struggle to achieve 100% accuracy in both low-level and high-level processes, with significant error rates in tasks such as sentence splitting, coreference resolution, named-entity recognition, summarization, and question answering, despite decades of research and substantial investment.
Innovation Solution
The implementation of Bounded-Scope Determinism (BSD) in neural network training, which involves creating novel pipelines of low-level NLP processes to achieve 100% accuracy, using Model Correction Interfaces (MCIs) that adhere to specific deterministic criteria for training inputs and outputs, ensuring precise transformations and uniform application of transformations across all outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If state-of-the-art neural network models are used for NLP tasks, then processing capability is improved, but accuracy remains below 100% for both low-level and high-level processes
Solution Approach 1:
The patent segments NLP processing into distinct low-level processes (sentence splitting, coreference resolution, named entity recognition) and high-level processes (summarization, question answering). By achieving 100% accuracy in the low-level segmented tasks through deterministic pipelines, the overall system reliability improves, allowing complex high-level tasks to be decomposed into accurate atomic operations.
Solution Approach 2:
Instead of attempting to achieve 100% accuracy across all NLP tasks simultaneously using monolithic models, the patent inverts the approach by first establishing 100% accurate deterministic pipelines for low-level tasks, then building high-level tasks on this foundation. This inversion of the traditional training paradigm enables accuracy where it was previously unattainable.
2Adaptability or versatility
If Large Language Models are used for high-level NLP processes, then processing versatility is improved, but real-world error rates increase to deep double digits
Solution Approach 1:
The patent introduces deterministic low-level NLP processes as intermediary components between text input and high-level NLP tasks. These intermediary processes (sentence splitting, coreference resolution, named entity recognition) operate with 100% accuracy and serve as reliable foundations upon which high-level tasks are built, mediating between raw text and complex processing requirements.
Solution Approach 2:
The patent segments the NLP pipeline into low-level deterministic processes and high-level probabilistic tasks. By isolating the low-level tasks into separate modules with guaranteed accuracy, the system maintains versatility for high-level tasks while eliminating the error propagation that occurs in monolithic LLM approaches.
3Manufacturing precision
If deterministic transformations are applied to training data, then manufacturing precision of NLP outputs is improved, but device complexity increases
Solution Approach 1:
The patent segments the training pipeline into distinct deterministic transformations (sentence splitting, coreference resolution, named entity recognition) that can be applied independently and uniformly. This segmentation allows each transformation to be optimized for precision while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent changes the fundamental parameters of training by applying deterministic transformations to create uniform training data representations. This parameter change in the training process enables 100% accuracy in low-level tasks while the complexity is managed through automated pipeline execution rather than manual intervention.
Data Source
AI summary
Systems and methods of accurate Natural Language Processing (NLP) for high-level NLP processes using novel pipelines of low-level NLP processes are disclosed, including a method for creating 100% accurate embodiments of the low-level NLP processes, resulting in 100% accurate implementations of the pipelined high-level NLP processes. The method for creating 100% accurate low-level NLP embodiments is called “Bounded-Scope Determinism” (BSD). The pipelines for producing accurate high-level NLP embodiments are called “Model Correction Interfaces” (MCIs). MCIs can be built using BSD low-level processes or they can be built using low-level processes known elsewhere in the art. When using non-BSD processes, accuracy is still profoundly increased. However, MCI embodiments that use BSD processes achieve 100% accuracy on high-level NLP tasks. For example, MCIs that use BSD lower-level NLP processes achieve 100% accurate Summarization, 100% accurate Question/Answering, 100% accurate Exposition, and more.