UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
📰 ArXiv cs.AI
Learn how UnIte improves domain adaptation in information retrieval by leveraging uncertainty-based iterative document sampling, enhancing the efficiency and quality of neural retriever generalization
Action Steps
- Apply UnIte to select documents for pseudo query generation
- Use uncertainty estimates to iteratively sample documents
- Evaluate the quality of the selected documents using diversity metrics
- Compare the performance of UnIte with existing document sampling methods
- Fine-tune the UnIte algorithm for specific domain adaptation tasks
Who Needs to Know This
Researchers and engineers working on information retrieval and domain adaptation can benefit from this approach to improve the performance of their neural retrievers
Key Insight
💡 Uncertainty-based document sampling can improve the efficiency and quality of domain adaptation in information retrieval
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📚 UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in IR 🚀 #informationretrieval #domainadaptation
Key Takeaways
Learn how UnIte improves domain adaptation in information retrieval by leveraging uncertainty-based iterative document sampling, enhancing the efficiency and quality of neural retriever generalization
Full Article
Title: UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
Abstract:
arXiv:2604.25142v1 Announce Type: cross Abstract: Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptation critically depend on which documents are selected for pseudo query generation. The existing document sampling method focuses on diversity but fails to capture model uncertainty. In contrast, we propose **Un**certainty-based **Ite**rative Document Sampling (UnIte) ad
Abstract:
arXiv:2604.25142v1 Announce Type: cross Abstract: Unsupervised domain adaptation generalizes neural retrievers to an unseen domain by generating pseudo queries on target domain documents. The quality and efficiency of this adaptation critically depend on which documents are selected for pseudo query generation. The existing document sampling method focuses on diversity but fails to capture model uncertainty. In contrast, we propose **Un**certainty-based **Ite**rative Document Sampling (UnIte) ad
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