Multi-LLM Guardrail Framework for Hallucination and Jailbreak Prevention
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Solution Overview
Problem
Current Generative AI systems suffer from hallucinations and jailbreaking, which compromise their accuracy and security, making them undesirable for many applications.
Innovation Solution
A multi-staged framework using multiple large language models (LLMs) to process user prompts, including a pre-processing LLM to filter and transform inputs, a generative LLM to generate responses, and a post-processing LLM to check for hallucinations and harmful content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single LLM is used for content generation, then the system is simple and fast, but it produces hallucinations and is vulnerable to jailbreaking
Solution Approach 1:
The system divides the content generation process into three separate LLM components: a pre-processing LLM for prompt evaluation and transformation, a generative LLM for content creation, and a post-processing LLM for output verification. This segmentation allows each model to specialize in specific tasks, improving overall reliability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The pre-processing and post-processing LLMs act as intermediaries between the user and the generative LLM. The pre-processing LLM transforms user prompts into safe, validated inputs, while the post-processing LLM verifies generated outputs for accuracy and safety. These intermediary models prevent direct interaction between users and the generative model, blocking jailbreaking attempts and reducing hallucinations.
2Object-affected harmful factors
If guardrails are added to prevent jailbreaking, then security improves, but the system becomes more complex and slower
Solution Approach 1:
The pre-processing LLM performs security validation and prompt transformation before the generative LLM processes the content. By evaluating and sanitizing inputs in advance, the system prevents malicious prompts from reaching the generative model, blocking jailbreaking attempts before they can execute. This preliminary security check integrates seamlessly into the workflow without requiring post-processing corrections.
Solution Approach 2:
The post-processing LLM analyzes generated outputs and provides feedback to ensure they meet safety and accuracy requirements. This feedback mechanism verifies that the generative LLM's output does not contain harmful content or hallucinations, creating a closed-loop system that continuously monitors and corrects potential issues while maintaining operational efficiency.
3Reliability
If multiple LLMs are used in a multi-staged framework, then hallucinations and jailbreaking are prevented, but system complexity and computational resources increase
Solution Approach 1:
The system segments the content generation process into three specialized LLM stages, with each model optimized for its specific function. The pre-processing LLM focuses on prompt validation and transformation, the generative LLM on content creation, and the post-processing LLM on output verification. This segmentation allows efficient resource allocation, where each model operates at optimal capacity for its designated task rather than requiring one oversized model to handle all functions.
Solution Approach 2:
The post-processing LLM performs verification on a selective basis, focusing computational resources on checking for hallucinations and safety issues only when necessary. Rather than re-generating entire outputs, the verification model performs targeted analysis of critical elements, applying excessive action only where needed to ensure reliability while conserving overall computational resources.
Data Source
AI summary
The disclosed embodiments include systems and methods configured to provide a Generative AI framework that uses the power of multiple LLMs by separating the generative aspect into multiple distinct large language models. In some disclosed embodiments, a first large language model evaluates an input prompt and transforms it if needed (e.g., in a first processing stage of the framework); a second large language model performs a generative function based on an input prompt it receives from the first large language model (e.g., in a second processing stage); and a third large language model analyzes and as necessary transforms the output of the second large language model to ensure accuracy, no hallucinations, and no harmful content in the final generated response to the input prompt (e.g., in a third processing stage).