Uncertainty-Based AI Model for Domain-Specific Information Extraction
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Solution Overview
Problem
Large language models (LLMs) struggle with performing specific language tasks, such as interpreting healthcare data, due to their general nature and lack of domain-specific fine-tuning.
Innovation Solution
A demonstration uncertainty-based AI model for open information extraction is introduced, which generates initial structured sentences using an LLM, determines structural similarities, identifies relational triplets, filters them based on demonstration uncertainty, and performs domain-specific tasks to assist decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large language model (LLM) is used for general language tasks, then text generation and translation capabilities are improved, but performance on specific domain tasks such as interpreting healthcare data deteriorates
Solution Approach 1:
The patent segments the information extraction process into multiple stages: initial structured sentence generation, structural similarity matching with training data, relational triplet identification, and uncertainty-based filtering. This segmentation allows the system to leverage general LLM capabilities while applying domain-specific refinement at each stage, resolving the contradiction between general adaptability and domain-specific reliability
Solution Approach 2:
The patent introduces structurally similar sentences from training data as an intermediary between the general LLM output and the final domain-specific results. These intermediary sentences serve as bridges that transfer domain-specific structural patterns to the LLM-generated content, improving domain task accuracy while maintaining the benefits of general language modeling
2Productivity
If initial structured sentences are generated using an LLM with an initial prompt, then sentence generation speed is improved, but accuracy in domain-specific contexts deteriorates
Solution Approach 1:
The patent performs preliminary action by generating initial structured sentences quickly using the LLM before applying domain-specific refinement. This preliminary generation maintains high productivity, while subsequent steps (structural similarity matching and uncertainty filtering) refine the accuracy without requiring complete regeneration, thus resolving the speed-accuracy trade-off
Solution Approach 2:
The patent implements feedback mechanisms where structurally similar sentences from training data provide guidance back to the initial LLM-generated sentences. The uncertainty calculation also provides feedback to identify and filter low-confidence extractions, thereby improving domain-specific accuracy while preserving the efficiency of initial LLM generation
3Loss of information
If relational triplets are identified from all generated sentences, then information extraction completeness is improved, but reliability of extracted information deteriorates due to inclusion of uncertain triplets
Solution Approach 1:
The patent changes the parameter of triplet selection from binary inclusion/exclusion to uncertainty-based filtering. By calculating demonstration uncertainty for each extracted triplet and comparing it against a threshold, the system dynamically adjusts which triplets are included, thereby maintaining completeness for high-confidence extractions while filtering out uncertain ones to preserve reliability
4Reliability
If domain-specific fine-tuning is applied to improve task performance, then domain task accuracy is improved, but model complexity and training requirements worsen
Solution Approach 1:
The patent applies local quality by using uncertainty-based filtering to identify and focus on domain-specific patterns where the LLM demonstrates lower confidence. This allows the system to apply domain-specific processing only where needed rather than uniformly across all inputs, improving domain accuracy without requiring comprehensive model fine-tuning and reducing overall system complexity
Data Source
AI summary
Systems and methods for a demonstration uncertainty-based artificial intelligence model for open information extraction. A large language model (LLM) can generate initial structured sentences using an initial prompt for a domain-specific instruction extracted from an unstructured text input. Structural similarities between the initial structured sentences and sentences from a training dataset can be determined to obtain structurally similar sentences. The LLM can identify relational triplets from combinations of tokens from generated sentences using and the structurally similar sentences. The relational triplets can be filtered based on a calculated demonstration uncertainty to obtain a filtered triplet list. A domain-specific task can be performed using the filtered triplet list to assist the decision-making process of a decision-making entity.