LLM Feedback Pipeline With Source Citation to Reduce Hallucinations
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Solution Overview
Problem
Large language models (LLMs) are prone to hallucinations, particularly when processing large volumes of input data with abstract prompts, leading to inaccurate outputs and reduced reliability in applications like product development.
Innovation Solution
Configuring LLMs to provide specific prompts, limiting input data size, and recording source citations to reduce hallucinations, and using a pipeline with stages like insight extraction, primary grouping, and mapping to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If LLMs process large volumes of input data with abstract prompts, then productivity is improved, but reliability deteriorates due to hallucinations
Solution Approach 1:
The patent segments the input data into smaller chunks and processes them through multiple stages (insight extraction, primary grouping, secondary grouping) rather than handling large volumes at once. This segmentation reduces hallucination while maintaining processing capacity through systematic breakdown of the data processing task.
Solution Approach 2:
The patent implements feedback mechanisms where LLM outputs are evaluated against source data, and hallucinations are detected and corrected. The system uses feedback loops to verify accuracy at each stage and adjust processing accordingly, maintaining reliability while preserving productivity.
2Adaptability or versatility
If LLMs use abstract prompts for flexibility, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The patent applies local quality by using abstract prompts at high levels for flexibility while implementing specific, detailed prompts at lower processing stages. Each stage has tailored prompts suited to its specific task, combining the adaptability of abstract prompting with the precision of specific prompting where needed.
Solution Approach 2:
The system dynamically adjusts prompt specificity based on processing stage and data characteristics. Prompts evolve from abstract to specific as data moves through processing stages, allowing the system to maintain both adaptability and precision throughout the complex processing pipeline.
3Ease of operation
If LLMs operate without source tracking, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The patent introduces source citation tracking as an intermediary mechanism that bridges the LLM processing stages with verifiable source data. This intermediary layer tracks information flow without significantly complicating operation, as the system automatically manages source tracking while providing a mechanism to verify accuracy and reduce hallucinations.
Data Source
AI summary
A large language model (LLM) can process customer or user feedback data and provide insights regarding how to improve a product or service. The LLM can be prone to hallucination, depending on how inputs are provided to the LLM and how the LLM is prompted. LLM hallucination can be reduced by constraining the size of the input and by prompting the LLM with specific prompts, including instructions to force the LLM to recite a location or source of input data, from which the LLM generates an output.