Expert Curation Systems for AI Hallucination Reduction
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Solution Overview
Problem
AI systems, particularly Large Language Models (LLMs) and Large Multimodal Models (LMMs), often hallucinate or provide incomplete and inaccurate information due to the quality of training data, posing risks in applications where inaccuracies can have dire consequences, especially in healthcare.
Innovation Solution
Implementing a hybrid 'expert-in-the-loop' system with tiered expert curation, including an adjudication board, advisory board, administrators, curators, and end-users, to validate, audit, and manage training data, ensuring relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI systems use large-scale training data to improve performance and speed, then productivity and processing capability are improved, but accuracy and reliability deteriorate due to hallucinations and misinformation
Solution Approach 1:
The patent segments the training data into multiple specialized databases (e.g., medical literature database, clinical trial database, guideline database) and organizes them hierarchically. Each database is curated by domain experts in specific fields, allowing the AI system to access targeted high-quality information while maintaining overall system productivity through structured data organization.
Solution Approach 2:
The patent introduces human expert curators as intermediaries between the training data and the AI system. These experts validate, annotate, and verify the accuracy of information before it is incorporated into the training databases, acting as a mediator that ensures reliability while allowing large-scale data processing to continue.
2Speed
If AI systems process information faster to provide timely results, then speed is improved, but accuracy deteriorates due to inability to verify information quality
Solution Approach 1:
The patent implements preliminary verification of information quality through expert curators before the data is used for training. This advance validation ensures that only accurate and verified information is incorporated into the training databases, allowing the AI system to provide fast responses without sacrificing verification accuracy.
Solution Approach 2:
The patent replaces manual verification processes with an automated AI-based verification system that uses multiple models to cross-check information consistency, detect hallucinations, and validate responses. This substitution maintains high verification standards while enabling faster processing speeds.
3Reliability
If expert curation is implemented to improve data quality, then reliability is improved, but system complexity increases due to multiple tiers of review
Solution Approach 1:
The patent divides the expert curation system into specialized tiers, with each tier responsible for specific domains or types of verification. This segmentation allows complex curation tasks to be distributed across multiple specialists rather than requiring a single monolithic review process, maintaining high reliability while organizing complexity into manageable segments.
Solution Approach 2:
The patent creates a multi-functional curation system where expert curators perform multiple roles including data validation, annotation, quality assessment, and continuous monitoring. This multi-functionality reduces the need for separate specialized systems for each task, thereby managing overall system complexity while maintaining comprehensive quality control.
4Measurement precision
If comprehensive validation by multiple individuals is performed to reduce hallucinations, then accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary filtering and pre-validation of data using automated systems and AI models before human expert review. This preliminary action reduces the volume of data requiring manual validation, thereby maintaining high information accuracy through comprehensive review while reducing the time loss associated with validating all data points manually.
Solution Approach 2:
The patent implements a system where the AI model performs self-validation through consistency checks, cross-referencing multiple sources, and detecting potential hallucinations. This self-service capability reduces the burden on human validators, allowing comprehensive accuracy checks without proportionally increasing validation time.
Data Source
AI summary
Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are expert curation systems and methods that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.