DRAGON: Domain-specific Robust Automatic Data Generation for RAG Optimization

Abstract

Abstract: Retrieval-augmented generation (RAG) can substantially enhance the performance of LLMs on knowledge-intensive tasks. Various RAG paradigms - including vanilla, planning-based, 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 retr

Research Question

How does the number of hops in multi-hop queries (e.g., 2-hop vs 3-hop from HotPotQA) affect the MRR@10 improvement from adversarial training on retriever robustness in RAG systems?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance