arxiv_ai 95% Match Research Paper AI Safety Researchers,LLM Developers,Machine Learning Engineers,AI Ethicists 2 weeks ago

LLM Safety Alignment is Divergence Estimation in Disguise

ai-safety › alignment

📄 Abstract

Abstract: We present a theoretical framework showing that popular LLM alignment methods, including RLHF and its variants, can be understood as divergence estimators between aligned (safe or preferred) and unaligned (harmful or less preferred) distributions. This perspective explains the emergence of separation in the latent space between safe and harmful prompts after alignment. As an application of our general divergence framework, we propose KLDO, a novel KL divergence-based alignment method, and empirically validate its effectiveness. We further show that using compliance-refusal datasets, rather than standard preference-based datasets, leads to stronger separation and improved safety alignment. Finally, to quantify the separation effect, we propose a distance-based metric in the prompt representation space, which also acts as a statistically significant indicator for model safety.

Authors (5)

Rajdeep Haldar

Ziyi Wang

Qifan Song

Guang Lin

Yue Xing

Submitted

February 2, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper presents a theoretical framework showing LLM alignment methods (like RLHF) are divergence estimators. It proposes KLDO, a KL divergence-based alignment method, and demonstrates that compliance-refusal datasets yield stronger separation and improved safety. A distance-based metric is introduced to quantify safety.

Business Value

Enables the development of safer and more reliable LLMs, crucial for widespread adoption in sensitive applications and reducing risks associated with harmful AI outputs.

Paper Metadata

Innovation Type

Theoretical/Algorithmic

Deployment Feasibility

High, integrates into existing LLM training and fine-tuning pipelines.

Limitations Addressed

Lack of a unified theoretical understanding of LLM alignment methods; need for better quantification of model safety.

Performance Gains

Stronger separation and improved safety alignment using compliance-refusal datasets.

Technical Tags

LLM safetyalignmentRLHFdivergence estimationKL divergencelatent space separationcompliance-refusal datasetsprompt representationmodel safety

Research Topics

AI SafetyLLM AlignmentReinforcement Learning from Human Feedback (RLHF)Machine Learning TheoryInterpretability

Methods & Architectures

Theoretical frameworkDivergence estimationKL divergence minimizationEmpirical validationDistance-based metric Large Language Models (LLMs)

Applications & Tasks

AI Safety LLM Development Natural Language Generation Ensuring LLM safety and alignmentReducing harmful outputsQuantifying model safety Aligning LLMs with human preferencesEstimating divergence between distributionsImproving model safety through divergence minimization

Datasets & Benchmarks

Datasets

compliance-refusal datasets, standard preference-based datasets

Latent space separationDistance-based metric for model safety

Related Fields

Artificial IntelligenceMachine LearningAI EthicsReinforcement LearningNatural Language Processing

Keywords

LLM safetyalignmentRLHFdivergenceKL divergenceAI safetylarge language modelsprompt engineeringmodel alignmenttheoretical frameworklatent spacesafety metrics

Academic Context

#AI Safety#LLM Alignment#Reinforcement Learning from Human Feedback (RLHF)#Machine Learning Theory#Interpretability

Commercial Potential

Potential Products

Safer LLM APIsAlignment toolkits for LLMsSafety evaluation platforms

Target Industries

TechnologyAI DevelopmentContent GenerationCustomer Service

Use Case Examples

Preventing LLMs from generating harmful contentEnsuring LLMs follow ethical guidelinesDeveloping more controllable AI assistants

Competitive Edge

Provides a novel theoretical perspective on LLM alignment, unifying existing methods under divergence estimation and proposing new algorithmic approaches.

Market Opportunity

Rapidly growing market for safe and aligned AI systems.

Revenue Models

Licensing of alignment techniquesconsulting services.

Resource Requirements

Compute Needs

Standard LLM training/fine-tuning infrastructure.

Data Requirements

Preference datasets, compliance-refusal datasets.

Deployment Constraints

Requires careful tuning and validation to ensure alignment goals are met without sacrificing utility.

Scalability

Scales with the size and complexity of the LLM.

Regulatory Considerations

Ethical AI guidelinespotential regulations on AI safety and alignment.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

View Full Paper Back to Papers