Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
arXiv:2603.26815v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems for financial document QA typically follow a chunk-based paradigm: documents are split into fragments, embedded, and retrieved by similarity. In structurally homogeneous corpora such as regulatory filings, this suffers from cross-document chunk confusion. Semantic File Routing (SFR), which uses LLM structured output to route queries to whole documents, reduces catastrophic failures but sacrifices targeted-chunk precision. We identify this robustness-precision trade-off on the FinDER benchmark
The proliferation of RAG systems in critical financial applications necessitates more robust and precise retrieval methods to avoid costly errors, making this research timely.
This work directly addresses a fundamental trade-off in RAG systems for financial data, impacting the reliability and accuracy of AI-driven financial analysis and decision-making.
The proposed Hybrid Document-Routed Retrieval offers a path to balancing robustness and precision in RAG, potentially enhancing the trustworthiness and utility of AI in sensitive domains.
- · Financial institutions adopting RAG
- · AI developers focused on enterprise solutions
- · Research in Retrieval-Augmented Generation
- · Companies relying on simplistic chunk-based RAG
- · Financial analysis firms with high error rates
Improved accuracy and reduced 'hallucinations' in financial AI applications using RAG.
Increased adoption of sophisticated RAG architectures across regulated industries due to enhanced reliability.
New AI-driven financial products and services become feasible with more trustworthy underlying retrieval.
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Read at arXiv cs.AI