Multi-LLM 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 utilizing 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, thereby preventing jailbreaking and hallucinations.
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 suffers from hallucinations and jailbreaking vulnerabilities
Solution Approach 1:
The patent divides the content generation system 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 distributing the complexity across multiple focused functions rather than requiring one monolithic complex model.
Solution Approach 2:
The patent introduces intermediary transformation layers between the user prompt and the generative LLM, and between the generative LLM and the final output. The pre-processing LLM acts as an intermediary to evaluate and transform prompts before they reach the generative model, while the post-processing LLM serves as an intermediary to verify and correct outputs. These intermediaries prevent direct interaction that could lead to hallucinations or jailbreaking.
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 patent implements preliminary evaluation and transformation of user prompts by the pre-processing LLM before the prompts reach the generative model. This preliminary action identifies and neutralizes potential jailbreaking attempts early in the process, preventing them from reaching the content generation stage. By handling security checks beforehand, the system maintains speed while improving security.
Solution Approach 2:
The post-processing LLM provides feedback verification of the generated content before it is returned to the user. This feedback mechanism checks for hallucinations and harmful content, and can request re-generation if issues are detected. The feedback loop ensures security without significantly impacting productivity, as most content passes verification on the first attempt.
3Reliability
If multiple LLMs are used in a multi-staged framework, then hallucinations and jailbreaking are prevented, but the system complexity and computational cost increase
Solution Approach 1:
The patent segments the content generation process into three distinct stages handled by separate LLMs: pre-processing, generation, and post-processing. This segmentation allows each model to be optimized for its specific task and run efficiently. The pre-processing LLM evaluates and transforms prompts, the generative LLM creates content, and the post-processing LLM verifies outputs. This division reduces the computational burden on any single model compared to using one large complex model for all functions.
Solution Approach 2:
The patent applies local quality by giving each LLM in the framework specialized functions and optimization. The pre-processing LLM is optimized for prompt evaluation and transformation tasks, the generative LLM for content creation, and the post-processing LLM for verification. Each model operates at its local optimal performance level for its specific task, improving overall system efficiency despite using multiple models.
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).