Hierarchical Language Model Inference for Scalable NLP Pipelines
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Solution Overview
Problem
Existing NLP systems lack scalability, flexibility, and consistency due to the manual creation and maintenance of language models, leading to inefficiencies in model development and deployment, and difficulty in expanding model suites to include new capabilities.
Innovation Solution
A hierarchical and extensible language model inference arrangement that organizes ML models according to a schema, allowing for efficient storage and loading of artifacts, and enables seamless integration of new models without requiring complete definitions, using a class hierarchy to support any current or future language models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual creation and maintenance of language models is used, then model customization and control are improved, but development efficiency and scalability deteriorate
Solution Approach 1:
The patent segments the language model development process into modular components including preprocessing modules, model definition modules, and inference modules. Each module can be independently developed, tested, and maintained, enabling parallel development workflows that improve productivity while preserving customization capabilities through modular assembly.
Solution Approach 2:
The patent creates a universal NLP pipeline framework that can accommodate multiple types of language models (transformers, LSTMs, CNNs) and various NLP tasks (classification, generation, translation) through a single standardized interface. This multi-functional design allows the system to handle diverse model requirements without requiring separate manual setup for each case, thereby improving development efficiency.
2Reliability
If comprehensive model definitions are maintained for all language models, then model consistency and reliability are improved, but system complexity and maintenance burden increase
Solution Approach 1:
The patent extracts common functionality and configuration patterns into a standardized model definition template that applies across different language model types. By separating model-specific parameters from universal configuration elements, the system maintains consistency through the standardized template while reducing complexity by eliminating redundant definitions for each model.
Solution Approach 2:
The patent introduces an intermediary model registry and configuration management system that mediates between model developers and the NLP pipeline. This intermediary layer standardizes model definitions, validates configurations, and manages versioning, thereby ensuring model consistency without requiring direct manual management of all model details, which would increase system complexity.
3Adaptability or versatility
If NLP pipelines are designed to work with every language model type, then adaptability and versatility are improved, but code complexity and maintenance difficulty increase
Solution Approach 1:
The patent designs a universal NLP pipeline with standardized interfaces and configuration schemas that can accommodate multiple language model types (transformers, LSTMs, CNNs) and various NLP tasks. The pipeline uses a unified model definition format and preprocessing interface that works across different model architectures, providing broad adaptability without requiring separate code paths for each model type.
Solution Approach 2:
The patent employs parameter-based model configuration where different language model types are instantiated by changing configuration parameters rather than modifying the underlying pipeline code. The system uses a parameterized approach where model architecture, hyperparameters, and task-specific settings are defined through configuration files and metadata, allowing the same pipeline code to adapt to different models through parameter variation rather than structural changes.
4Adaptability or versatility
If new language models are added to the model suite, then model capabilities and versatility are improved, but development time and integration effort increase
Solution Approach 1:
The patent establishes preliminary standardized model definition templates, configuration schemas, and integration protocols before new models need to be added. By pre-defining the interface contracts, validation rules, and deployment workflows, the system enables new language models to be integrated by simply providing their configuration parameters and artifacts, eliminating the need for time-consuming custom integration work for each new model.
Solution Approach 2:
The patent implements a self-service model registration and validation system that automatically validates new model definitions against the standardized schema, registers them in the model catalog, and configures them for deployment without requiring manual intervention from developers. This automated self-service approach allows new models to be added rapidly while maintaining consistency with the existing NLP pipeline through automatic validation and registration.
Data Source
AI summary
At least one processor can receive an indication of processing to be performed in a natural language processing (NLP) machine learning (ML) pipeline, determine a first model class and a second model class for the processing. The first model class can be at a first hierarchical level of an ML hierarchy schema, and the second model class can be at a second hierarchical level of the ML hierarchy schema. The at least one processor can prepare a dictionary in a memory in communication with the at least one processor, which can comprise populating the dictionary with all required artifacts of the first model class and a subset of required artifacts of the second model class, wherein the second model class requires at least one of the required artifacts of the first model class. The at least one processor can perform NLP on text using the ML model and the dictionary.