LLM Self-Guardrail via NLI Anti-Hypothesis Prompting
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Solution Overview
Problem
Existing Large Language Models (LLMs) often generate responses that are inappropriate or offensive, posing a risk to users and limiting their application.
Innovation Solution
The implementation of a self-guardrail mechanism using Natural Language Inference (NLI) to identify potentially dangerous outputs and apply anti-hypothesis prompt edits to ensure appropriate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Large Language Models generate responses freely, then responsiveness and efficiency are improved, but inappropriate or offensive content may be produced
Solution Approach 1:
The system performs preliminary actions by inserting guardrail hypotheses and anti-hypothesis prompt edits into the prompt before the LLM generates its response. This preemptive approach allows the model to self-correct potentially harmful outputs before they are produced, maintaining responsiveness while preventing inappropriate content generation.
Solution Approach 2:
The guardrail mechanism acts as an intermediary between the user prompt and the LLM response generation. By introducing validation hypotheses and anti-hypothesis edits as intermediate processing steps, the system filters and controls the output without completely blocking the generative process, thus maintaining efficiency while ensuring safety.
2Reliability
If guardrail mechanisms are added to filter LLM responses, then safety and appropriateness are improved, but system complexity increases
Solution Approach 1:
The LLM performs self-service by generating its own guardrail hypotheses and anti-hypothesis prompt edits through natural language inference. Instead of requiring external validation systems, the model uses its own capabilities to identify and correct potentially harmful content, simplifying the overall system architecture while maintaining high safety standards.
Solution Approach 2:
The system dynamically changes prompt parameters by inserting or removing guardrail hypotheses and anti-hypothesis edits based on the specific context. This flexible approach allows the system to adjust the level of guardrail enforcement for different scenarios, maintaining safety without uniformly increasing complexity across all operations.
3Reliability
If Natural Language Inference is used to validate responses, then content appropriateness is improved, but processing time increases
Solution Approach 1:
The system implements selective validation by skipping detailed NLI analysis for obviously safe responses and only performing thorough validation when guardrail hypotheses are triggered. This allows the system to maintain high content appropriateness for complex cases while minimizing processing time for straightforward interactions, effectively rushing through unnecessary validation steps.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods to self-guardrail large language model responses are disclosed. An example apparatus includes interface circuitry, instructions, and programmable circuitry to at least one of execute or instantiate the instructions to access a first response message provided by a first large language model, the first response message generated based on an initial prompt, cause a second large language model to determine the first response message is inappropriate, modify the initial prompt to create a modified prompt, and provide the modified prompt to the first large language model to trigger generation of a second response message.