arxiv_ai 98% Match Research Paper AI Safety Researchers,LLM Developers,ML Engineers,Product Managers in AI 2 weeks ago

GuardReasoner: Towards Reasoning-based LLM Safeguards

ai-safety › alignment

📄 Abstract

Abstract: As LLMs increasingly impact safety-critical applications, ensuring their safety using guardrails remains a key challenge. This paper proposes GuardReasoner, a new safeguard for LLMs, by guiding the guard model to learn to reason. Concretely, we first create the GuardReasonerTrain dataset, which consists of 127K samples with 460K detailed reasoning steps. Then, we introduce reasoning SFT to unlock the reasoning capability of guard models. In addition, we present hard sample DPO to further strengthen their reasoning ability. In this manner, GuardReasoner achieves better performance, explainability, and generalizability. Extensive experiments and analyses on 13 benchmarks of 3 guardrail tasks demonstrate its superiority. Remarkably, GuardReasoner 8B surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on average. We release the training data, code, and models with different scales (1B, 3B, 8B) of GuardReasoner : https://github.com/yueliu1999/GuardReasoner/.

Authors (12)

Yue Liu

Hongcheng Gao

Shengfang Zhai

Yufei He

Jun Xia

Zhengyu Hu

+6 more

Submitted

January 30, 2025

arXiv Category

cs.CR

arXiv PDF Code

Key Contributions

GuardReasoner proposes a novel reasoning-based approach to LLM safeguards by training guard models to reason, significantly improving their performance, explainability, and generalizability. It introduces a large-scale reasoning dataset (GuardReasonerTrain) and employs reasoning SFT and hard sample DPO to enhance guard model capabilities, outperforming existing methods.

Business Value

Enhances the trustworthiness and deployability of LLMs in sensitive applications by providing robust, explainable, and generalizable safety mechanisms, reducing risks associated with harmful outputs.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, with models released in various scales (1B, 3B, 8B) and code available.

Limitations Addressed

Limited effectiveness of traditional LLM guardrails,Lack of explainability in safety mechanisms,Poor generalizability of safety models,Difficulty in ensuring safety in critical applications

Performance Gains

GuardReasoner 8B surpasses GPT-4o+CoT by 5.74% and LLaMA Guard 3 8B by 20.84% F1 score on average.

View Code on GitHub

Technical Tags

LLM SafeguardsReasoningGuard ModelsSupervised Fine-Tuning (SFT)Direct Preference Optimization (DPO)ExplainabilityGeneralizabilitySafety-Critical ApplicationsHard Sample MiningBenchmarking

Research Topics

AI SafetyLarge Language ModelsModel AlignmentExplainable AIRobustnessMachine Learning

Methods & Architectures

Reasoning-based SFTHard Sample DPOCreation of GuardReasonerTrain dataset LLMsGuard Models

Applications & Tasks

Safety-Critical AI Applications Large Language Model Deployment Ensuring LLM SafetyImproving Guardrail EffectivenessEnhancing Model ExplainabilityAchieving Generalizability in Safety Models LLM Safety GuardrailsContent ModerationHarmful Content DetectionReasoning Verification

Datasets & Benchmarks

Datasets

GuardReasonerTrain

Benchmarks

13 benchmarks across 3 guardrail tasks

F1 score

Related Fields

AI SafetyLarge Language ModelsMachine LearningNatural Language ProcessingExplainable AIModel Alignment

Keywords

LLM SafetyGuardrailsReasoningAI AlignmentLarge Language ModelsExplainabilityGeneralizabilitySFTDPOSafety-Critical AIContent ModerationRobustness

Academic Context

#AI Safety#Large Language Models#Model Alignment#Explainable AI#Robustness#Machine Learning

Commercial Potential

Potential Products

LLM safety modulesContent filtering servicesAI risk assessment tools

Target Industries

TechnologyHealthcareFinanceCustomer ServiceMedia

Use Case Examples

Preventing LLMs from generating hate speech or misinformationEnsuring AI assistants provide safe and ethical responsesBuilding secure AI applications for sensitive domains

Competitive Edge

Outperforms existing LLM safety solutions like LLaMA Guard and approaches using GPT-4o+CoT in terms of performance, explainability, and generalizability.

Market Opportunity

Rapidly growing market for LLM safety and alignment solutions.

Revenue Models

Commercial licensing of enhanced safety modelsconsulting services.

Resource Requirements

Compute Needs

Moderate to High (depending on model size)

Data Requirements

Large, diverse dataset for training reasoning-based guard models.

Deployment Constraints

Integration complexity with existing LLM pipelines.

Scalability

Models available in multiple sizes (1B, 3B, 8B) indicating scalability considerations.

Regulatory Considerations

Compliance with AI safety regulationsdata privacy.

Production Readiness

Maturity Level

Research (with released models and code)

Time to Market

1-2 years

Licensing

Open source (Apache 2.0 likely, based on typical GitHub releases)

Patent Potential

Moderate

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