Year: 2026
Type: conference-paper
Abstract: Retrieval-augmented generation (RAG) can substantially enhance the performance of LLMs on knowledge-intensive tasks.Various RAG paradigms-including vanilla, planningbased, and iterative RAG-all depend on a robust retriever, yet existing retrievers rely heavily on public knowledge and often falter when faced with domain-specific queries.To address these limitations, we introduce DRAGON, a framework that combines a data-construction modeling approach with a scalable synthetic data-generation pipeline, specifically designed to optimize domain-specific retrieval performance and bolster retriever robustness.To evaluate RAG performance on domain-specific RAGs, we propose DRAGONBENCH, a benchmark spanning 8 domain-specific document collections across 4 distinct fields and featuring a wide spectrum of query complexities, answerability, and hop numbers.Leveraging DRAGON, we generate a large-scale synthetic dataset-encompassing both single-hop and multi-hop queries-to enrich retriever training.Extensive experiments demonstrate that retrievers trained on this data yield significant performance gains and exhibit strong cross-domain generalization.Moreover, when our optimized retrievers are integrated into vanilla, planningbased, and iterative RAG paradigms, we observe consistent end-to-end improvements in system accuracy.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140109918