A Flexible Large Language Models Guardrail Development Methodology Applied to Off-Topic Prompt Detection

2024-11-20

arXiv.org

AI Summary

Large language models (LLMs) are vulnerable to off-topic misuse—including jailbreaking and harmful prompting—while existing safety guardrails rely heavily on real-world data, suffer from high false-positive rates, and exhibit poor generalization. Method: We propose a data-agnostic guardrail construction paradigm that frames off-topic detection as a relevance classification task between system and user prompts. Leveraging LLMs to generate diverse, synthetically curated prompts—guided by qualitative problem-space characterization—we enable zero-shot or few-shot detection without real abuse examples. Contributions/Results: We introduce the first real-data-free guardrail development framework; achieve natural generalization across diverse misuse scenarios; and publicly release a high-quality synthetic dataset alongside a lightweight guard model. Experiments demonstrate significantly reduced false positives, outperformance over heuristic baselines, and strong pre-production readiness with high deployment adaptability.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: Safety and RobustnessComputer Vision: Large Vision Models

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rates, limited adaptability, and the impracticality of requiring real-world data that is not available in pre-production. In this paper, we introduce a flexible, data-free guardrail development methodology that addresses these challenges. By thoroughly defining the problem space qualitatively and passing this to an LLM to generate diverse prompts, we construct a synthetic dataset to benchmark and train off-topic guardrails that outperform heuristic approaches. Additionally, by framing the task as classifying whether the user prompt is relevant with respect to the system prompt, our guardrails effectively generalize to other misuse categories, including jailbreak and harmful prompts. Lastly, we further contribute to the field by open-sourcing both the synthetic dataset and the off-topic guardrail models, providing valuable resources for developing guardrails in pre-production environments and supporting future research and development in LLM safety.

Problem

• Detecting off-topic prompts in LLMs to prevent misuse
• Overcoming high false-positive rates and limited adaptability in guardrails
• Generalizing guardrails to other misuse categories like jailbreak prompts

Innovation

• Data-free synthetic dataset generation for guardrails
• LLM-based off-topic prompt classification method
• Open-sourced guardrail models and datasets