Papers with DRAGONBench

1 papers
DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization (2026.findings-eacl)

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Challenge: Existing retrieval-augmented generation paradigms rely heavily on public knowledge . Existing RAGs reliant on public information and often falter when faced with domain-specific queries.
Approach: They propose a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline to optimize domain-specific retrieval performance.
Outcome: The proposed framework optimizes domain-specific retrieval performance and bolsters retriever robustness.

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