arXiv Computation and Language By Hridya Dhulipala, Rajesh Ombase, Michael Wang, Tien N. Nguyen

W-RAG: Source-Aware Retrieval for Enterprise Document Generation from Heterogeneous Knowledge Bases

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W-RAG is a source-aware retrieval framework designed for enterprise document generation from heterogeneous knowledge bases. It uses ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to balance evidence from diverse sources. A new dataset covering multiple document types and industry domains demonstrates that W-RAG improves document coverage and generation quality compared to standard RAG pipelines.

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arXiv AI
Sep 16

ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation

The paper introduces ORDER, a task‑conditioned retrieval‑augmented generation framework that dynamically adapts both indexing and retrieval strategies to each incoming query. It first clusters questions to learn cluster‑specific chunking, metadata filtering, and reranking settings, then routes queries to the appropriate pre‑built index via nearest‑centroid assignment. Additionally, a supervised query router predicts relevant collections and a Uniform Multi‑source Sampler distributes the retrieval budget evenly across selected sources, yielding superior performance on heterogeneous historical archives compared to existing RAG systems.

By Aur\'elien Pellet (LRE), Julien Perez, Marie Puren