DEMONSTRATION UNCERTAINTY-BASED ARTIFICIAL INTELLIGENCE MODEL FOR OPEN INFORMATION EXTRACTION
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United States of America
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Mar 6, 2025
app pub date -
Aug 28, 2024
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Aug 28, 2024
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Abstract
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.
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