LLM Entity Extraction Pipeline for Cross-Domain Sentiment Mapping
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Solution Overview
Problem
Existing natural language processing (NLP) models struggle to scale across multiple domains and categories, requiring manual tuning and maintenance, which is inefficient and undesirable for handling diverse data sources.
Innovation Solution
A machine-learning pipeline utilizing large language models (LLMs) for entity extraction, disambiguation, and sentiment analysis, incorporating runtime optimizations like parallel processing, selective inference, and intelligent caching to handle structured and unstructured data from various sources, generating interpretable outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple NLP models are fine-tuned for different industries or categories, then domain-specific performance is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent applies a single foundation model that can be dynamically adapted to multiple domains and categories through prompt engineering and parameter-efficient fine-tuning techniques. This universal model replaces the need for maintaining separate NLP models for different industries, reducing complexity while preserving domain-specific performance through flexible adaptation mechanisms.
Solution Approach 2:
The system employs dynamic adaptation capabilities where the foundation model can be quickly reconfigured for different domains through parameter-efficient fine-tuning and prompt adjustments. This dynamic reconfiguration allows the same model to serve multiple purposes without requiring manual retraining or complex model switching infrastructure.
2Measurement precision
If NLP models are manually trained on unstructured datasets, then measurement precision is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent utilizes pre-trained foundation models that have already been extensively trained on diverse unstructured datasets during their development phase. This preliminary training eliminates the need for manual training on each specific domain, allowing rapid deployment and adaptation to new categories while maintaining high measurement precision through the model's pre-learned patterns and relationships.
Solution Approach 2:
The system employs parameter-efficient fine-tuning techniques that create lightweight adaptations of the foundation model rather than training entirely new models. This copying approach preserves the valuable knowledge from the original foundation model while making minimal modifications to adapt to specific domains, significantly reducing training time and computational resources required.
3Productivity
If large language models process large datasets, then productivity is improved, but use of energy and computational expense increase
Solution Approach 1:
The patent applies parameter-efficient fine-tuning and prompt engineering techniques that enable the foundation model to achieve high productivity on large datasets without requiring full-scale retraining. By using only the necessary computational resources for inference and minimal parameter updates, the system maintains high processing throughput while significantly reducing energy consumption compared to training entirely new models for each domain.
Data Source
AI summary
A method comprising retrieving one or more requirements of knowledge to be extracted; generating a prompt corresponding to the one or more requirements; validating the prompt by executing a large language model using the prompt and evaluating the response predicted by the large language model; fine-tuning the large language model using validation data generated as a result of validating the prompt; and executing the fine-tuned large language model using a text corpus to analyze one or more item reviews and generate a pair of at least one entity and a respective relationship sentiment value for the entity.