arXiv (All)
open access
Towards Expert Financial QA via Self-Improving RAG
Expert-level financial question answering requires both grounded verification to catch numeric hallucinations and audit trails for regulatory compliance, attributes that standard single-pass RAG systems lack. We take a step toward this goal with Self-Improving RAG, a framework that decomposes docume
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Enhancing Financial Question Answering: A Novel Benchmark Dataset of Banks' financial statements#985268
- IGT @ FinMMEval 2026 Task 2: Question-Type Prompting with Targeted Extraction for Multilingual Financial QA#985524
- Self-reference Self-explained#775125
- FinInteract: Benchmarking Clarification and Intent Integration in Ambiguous Financial Question Answering#1050191
- Beyond Benchmark Scores: How Synthetic and Authentic Query Distributions Diverge in RAG Evaluation#1084523
Record · ID 1063994
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive