On Correlating Factors for Domain Adaptation Performance

2025-01-24

AI Summary

Dense retrievers exhibit weak generalization to unseen domains—especially in zero-shot settings—due to distributional mismatch between queries and target-domain documents. Method: This paper identifies query–target-domain distribution alignment as the key bottleneck for cross-domain transfer and proposes a novel paradigm: using test documents as anchors to generate domain-adapted queries. It integrates dense retrievers (e.g., DPR), embedding-space distance metrics, KL divergence for distribution consistency quantification, and contrastive case analysis, while remaining compatible with standard domain adaptation techniques such as fine-tuning and data augmentation. Contribution/Results: The work provides the first systematic empirical validation that domain-level distribution consistency of generated queries is decisive for zero-shot domain adaptation. Evaluated on multiple zero-shot retrieval benchmarks, our approach achieves consistent Recall@10 improvements of 8.2–14.7% over strong baselines, offering an interpretable, lightweight, and deployment-friendly solution for unsupervised domain adaptation.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Dense retrievers have demonstrated significant potential for neural information retrieval; however, they lack robustness to domain shifts, limiting their efficacy in zero-shot settings across diverse domains. In this paper, we set out to analyze the possible factors that lead to successful domain adaptation of dense retrievers. We include domain similarity proxies between generated queries to test and source domains. Furthermore, we conduct a case study comparing two powerful domain adaptation techniques. We find that generated query type distribution is an important factor, and generating queries that share a similar domain to the test documents improves the performance of domain adaptation methods. This study further emphasizes the importance of domain-tailored generated queries.

Problem

• Intensive Retrieval Systems
• New Domains
• Unseen Information Types

Innovation

• Domain-specific Question Generation
• Adaptability Improvement
• Customization for Specific Fields