CAR is a new retrieval objective that targets the currently active authority set rather than most-similar documents, with theorems on coverage conditions and evaluations showing two-stage methods outperform dense retrieval on authority-governed datasets.
FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4representative citing papers
Hybrid Document-Routed Retrieval (HDRR) filters financial filings with LLM document routing then scopes chunk retrieval, beating pure chunk and pure file routing on FinDER accuracy and efficiency.
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
Neural reranking in a hybrid RAG system raises high-quality answer rates from 33.5% to 49.0% on financial report questions.
citing papers explorer
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Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge
CAR is a new retrieval objective that targets the currently active authority set rather than most-similar documents, with theorems on coverage conditions and evaluations showing two-stage methods outperform dense retrieval on authority-governed datasets.
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Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off
Hybrid Document-Routed Retrieval (HDRR) filters financial filings with LLM document routing then scopes chunk retrieval, beating pure chunk and pure file routing on FinDER accuracy and efficiency.
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MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration
MimirRAG, a multi-agent RAG framework with metadata integration and table-aware chunking, reaches 89.3% accuracy on FinanceBench and outperforms prior baselines for financial document retrieval.
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Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis
Neural reranking in a hybrid RAG system raises high-quality answer rates from 33.5% to 49.0% on financial report questions.