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W-RAG framework improves enterprise document generation with source-aware retrieval

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced W-RAG, a novel framework designed to enhance enterprise document generation by improving retrieval-augmented generation (RAG) pipelines. Unlike standard RAG that uses a single similarity function for all sources, W-RAG employs source-aware retrieval. This method includes ontology-guided retrieval, local ranking within individual knowledge bases, and source-level weighting to ensure balanced context composition. Experiments with a new dataset demonstrate W-RAG's effectiveness in improving document coverage and generation quality for heterogeneous enterprise knowledge bases. AI

IMPACT Enhances enterprise document generation by improving the factual grounding and coverage of LLM outputs from heterogeneous knowledge bases.

RANK_REASON The cluster contains an academic paper detailing a new method for retrieval-augmented generation.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

W-RAG framework improves enterprise document generation with source-aware retrieval

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The cluster contains an academic paper detailing a new method for retrieval-augmented generation.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Hridya Dhulipala, Rajesh Ombase, Michael Wang, Tien N. Nguyen ·

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

    arXiv:2608.22081v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enables large language models to incorporate external knowledge during generation, improving factual grounding and domain adaptability. However, existing RAG pipelines assume that evidence retr…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Tien N. Nguyen ·

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

    Retrieval-Augmented Generation (RAG) enables large language models to incorporate external knowledge during generation, improving factual grounding and domain adaptability. However, existing RAG pipelines assume that evidence retrieved from multiple repositories can be ranked glo…

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