arxiv_cl 95% Match Research Paper ML Researchers,AI Alignment Researchers,NLP Engineers,Developers of LLMs 20 hours ago

On Extending Direct Preference Optimization to Accommodate Ties

large-language-models › alignment

📄 Abstract

Abstract: We derive and investigate two DPO variants that explicitly model the possibility of declaring a tie in pair-wise comparisons. We replace the Bradley-Terry model in DPO with two well-known modeling extensions, by Rao and Kupper and by Davidson, that assign probability to ties as alternatives to clear preferences. Our experiments in neural machine translation and summarization show that explicitly labeled ties can be added to the datasets for these DPO variants without the degradation in task performance that is observed when the same tied pairs are presented to DPO. We find empirically that the inclusion of ties leads to stronger regularization with respect to the reference policy as measured by KL divergence, and we see this even for DPO in its original form. We provide a theoretical explanation for this regularization effect using ideal DPO policy theory. We further show performance improvements over DPO in translation and mathematical reasoning using our DPO variants. We find it can be beneficial to include ties in preference optimization rather than simply discard them, as is done in common practice.

Key Contributions

This paper introduces two variants of Direct Preference Optimization (DPO) that explicitly model ties in pairwise comparisons, using extensions to the Bradley-Terry model. Experiments show these variants improve performance in NMT and summarization, provide stronger regularization, and offer theoretical explanations for this effect, outperforming standard DPO in translation and mathematical reasoning.

Business Value

Enables more robust and accurate alignment of LLMs with human preferences, leading to better-performing and more reliable AI systems for various applications.

Paper Metadata

Innovation Type

Algorithmic innovation

Deployment Feasibility

High, as it directly improves a key alignment technique (DPO) used in LLM development.

Limitations Addressed

The inability of standard DPO to effectively handle ties in pairwise preference data, which can lead to performance degradation or suboptimal alignment.

Performance Gains

Performance improvements over standard DPO in translation and mathematical reasoning, stronger regularization.

Technical Tags

Direct Preference Optimization (DPO)preference learningties in comparisonsregularizationneural machine translationsummarizationmathematical reasoningKL divergence

Research Topics

Reinforcement Learning from Human Feedback (RLHF)AI AlignmentPreference LearningNatural Language GenerationModel Optimization

Methods & Architectures

DPO variants for tiesBradley-Terry model extensions (Rao & Kupper, Davidson)Empirical evaluation on NMT and summarizationTheoretical analysis of regularization LLMs

Applications & Tasks

Natural Language Generation AI Alignment Machine Translation Text Summarization Handling ties in preference dataImproving DPO robustnessEnhancing regularization in preference optimization Aligning LLMs with human preferencesImproving NMT performanceEnhancing summarization qualityImproving mathematical reasoning

Related Fields

Machine LearningDeep LearningNatural Language ProcessingReinforcement LearningAI Alignment

Keywords

DPOpreference optimizationtiespairwise comparisonalignmentLLMregularizationNMTsummarizationmathematical reasoningBradley-Terry modelKL divergence

Academic Context

#Reinforcement Learning from Human Feedback (RLHF)#AI Alignment#Preference Learning#Natural Language Generation#Model Optimization

Commercial Potential

Potential Products

Improved LLM alignment toolsMore robust preference learning frameworks

Target Industries

TechnologyAI DevelopmentSaaS

Use Case Examples

Training LLMs to generate more helpful and harmless responses by better incorporating human feedback, even when preferences are ambiguous.Developing better machine translation systems that align with user preferences.

Competitive Edge

This work extends DPO to handle a common real-world data issue (ties), making it more practical and robust, and demonstrating empirical and theoretical advantages over standard DPO.

Market Opportunity

The market for LLM alignment and fine-tuning is rapidly expanding.

Revenue Models

Licensing of improved alignment algorithmsintegration into commercial LLM development platforms.

Resource Requirements

Compute Needs

Moderate to high, for training and evaluating LLMs on NMT and summarization tasks.

Data Requirements

Datasets with pairwise comparisons, including explicit labels for ties.

Deployment Constraints

Requires careful data annotation to include ties.,Tuning of the new DPO variants might be necessary.

Scalability

The method is designed for LLMs, implying scalability to large models.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into LLM training pipelines

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