arxiv_cl 95% Match Research Paper AI Safety Researchers,Machine Learning Engineers,LLM Developers,AI Ethics Professionals 2 weeks ago

Leveraging Robust Optimization for LLM Alignment under Distribution Shifts

large-language-models › alignment

📄 Abstract

Abstract: Preference alignment methods are increasingly critical for steering large language models (LLMs) to generate outputs consistent with human values. While recent approaches often rely on synthetic data generated by LLMs for scalability and cost-efficiency reasons, this reliance can introduce distribution shifts that undermine the nuanced representation of human preferences needed for desirable outputs. In this paper, we propose a novel distribution-aware optimization framework that improves preference alignment despite such shifts. Our approach first leverages well-learned classifiers to assign a calibration value to each training sample, quantifying its alignment with the target human-preferred distribution. These values are then incorporated into a robust optimization objective that minimizes the worst-case loss over regions of the data space most relevant to human preferences. By explicitly focusing optimization on the target distribution, our approach mitigates the impact of distributional mismatch and improves the generation of responses that better reflect intended values.

Authors (5)

Mingye Zhu

Yi Liu

Zheren Fu

Yongdong Zhang

Zhendong Mao

Submitted

April 8, 2025

arXiv Category

cs.CL

arXiv PDF

Key Contributions

Proposes a novel distribution-aware robust optimization framework to improve LLM preference alignment despite distribution shifts. It uses calibration values to quantify sample alignment and minimizes worst-case loss, focusing optimization on the target distribution to mitigate negative impacts.

Business Value

Enhances the reliability and trustworthiness of LLMs by ensuring their outputs consistently align with human values, even when faced with evolving or imperfect training data, crucial for sensitive applications.

Paper Metadata

Innovation Type

Algorithmic/Methodological

Deployment Feasibility

Moderate to High, depending on the complexity of integration into existing alignment pipelines and computational overhead.

Limitations Addressed

Undermining of preference alignment due to distribution shifts introduced by using synthetic data generated by LLMs.

Performance Gains

Improved preference alignment under distribution shifts compared to standard methods.

Technical Tags

LLM AlignmentRobust OptimizationDistribution ShiftsPreference LearningHuman ValuesMachine LearningReinforcement Learning

Research Topics

Robustness in LLM AlignmentHandling Distributional ShiftsPreference ModelingAI Safety

Methods & Architectures

Distribution-aware OptimizationRobust Optimization FrameworkCalibration Value AssignmentWorst-case Loss MinimizationSupervised Fine-tuning (implied) Large Language Models (LLMs)

Applications & Tasks

Artificial Intelligence Machine Learning AI Safety LLM preference alignmentDistribution shifts in training dataEnsuring LLM outputs align with human values Aligning LLM outputs with human preferencesImproving robustness of alignment methods

Related Fields

Machine LearningArtificial IntelligenceAI SafetyOptimization TheoryReinforcement Learning

Keywords

LLM AlignmentRobust OptimizationDistribution ShiftPreference LearningHuman ValuesAI SafetyMachine LearningReinforcement LearningCalibrationTrustworthy AI

Academic Context

#Robustness in LLM Alignment#Handling Distributional Shifts#Preference Modeling#AI Safety

Commercial Potential

Potential Products

More reliable and safer LLM servicesTools for robust LLM alignment

Target Industries

TechnologyAI Development any industry using LLMs

Use Case Examples

Aligning chatbots to be helpful and harmlessEnsuring AI assistants follow ethical guidelinesDeveloping safer AI systems for critical applications

Competitive Edge

Offers a more robust approach to LLM alignment compared to methods that are sensitive to data distribution shifts.

Market Opportunity

Growing market for AI safety and alignment solutions.

Revenue Models

Licensing the alignment frameworkconsulting services.

Resource Requirements

Compute Needs

Likely significant, involving training and optimization processes for LLMs.

Data Requirements

Preference data, potentially synthetic data, and well-learned classifiers.

Deployment Constraints

Computational cost of robust optimization; need for accurate calibration classifiers.

Scalability

Scalability depends on the efficiency of the robust optimization algorithm and the size of the LLM.

Regulatory Considerations

AI safety regulationsethical AI guidelines.

Production Readiness

Maturity Level

Research/Development

Time to Market

2-4 years for integration into production systems.

Licensing

Likely research-focused, potential for commercial licensing if successful.

Patent Potential

Moderate, for the novel optimization framework.

View Full Paper Back to Papers