Topic Maps for Constrained RAG and Hallucination Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current generative AI agents using large language models (LLMs) face challenges with non-deterministic outputs, hallucinations, and the inability to easily incorporate new information due to their static knowledge base and resource-intensive training requirements, which affects consistency and accuracy.
Innovation Solution
Implementing topic maps that pre-compute and curate relevant information, allowing constrained retrieval augmented generation by using topic maps to scope queries and limit the knowledge base to up-to-date and relevant information, thereby reducing hallucinations and improving response consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs rely on static pre-trained knowledge, then the system complexity is reduced, but factual accuracy and up-to-date information deteriorate
Solution Approach 1:
The system segments the knowledge base into static pre-trained knowledge and dynamic retrieved knowledge. The LLM handles general patterns while a separate retrieval component handles up-to-date factual information, resolving the contradiction by dividing functionality across components.
Solution Approach 2:
A retrieval augmented generation component acts as an intermediary between the LLM and external knowledge sources. This mediator provides factual accuracy and current information without requiring the LLM itself to be complex or continuously retrained.
2Reliability
If LLMs are retrained with new information, then up-to-date information is improved, but computational overhead and training resources worsen
Solution Approach 1:
The system performs preliminary organization of knowledge into structured topic maps with semantic vector embeddings before runtime. This pre-computation allows rapid retrieval of up-to-date information during queries without requiring computationally intensive retraining of the LLM.
Solution Approach 2:
The system substitutes the mechanical retraining process with a lighter-weight retrieval mechanism. Instead of retraining neural network weights computationally, the system uses semantic search and vector similarity to efficiently access updated information.
3Adaptability or versatility
If the knowledge base is expanded to cover more topics, then adaptability is improved, but information retrieval precision deteriorates
Solution Approach 1:
The expanded knowledge base is segmented into discrete topic maps, each representing a specific domain or subject area. This segmentation allows the system to maintain broad adaptability across topics while ensuring retrieval precision within each topic through dedicated semantic embeddings and structured organization.
Data Source
AI summary
Current generative AI systems using large language models (LLMs) face challenges including non-deterministic outputs, hallucinations, outdated information, and resource-intensive training. This disclosure introduces topic maps for constrained retrieval augmented generation to address these issues. The technique leverages existing LLMs while constraining outputs to specific, user-defined content domains. Topic maps, composed of topic names, descriptions, and relevant resource references, create a curated knowledge base that guides agent responses. This approach reduces hallucinations, improves consistency, and allows for dynamic updates without model retraining. The method involves receiving a query, identifying relevant topic maps, transmitting the query and references to an AI agent, and generating constrained responses. By providing a structured, updatable knowledge framework, this method enhances the accuracy, reliability, and adaptability of generative AI systems.