{"ID":2870989,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12042","arxiv_id":"2509.12042","title":"FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval","abstract":"Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Question Tree), and a two-stage cross-encoder reranker. This design aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection. Evaluated on full 10-Ks with queries aligned to the FinQA dataset, FinGEAR delivers consistent gains in precision, recall, F1, and relevancy, improving F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems, while also increasing downstream answer accuracy with a fixed reader. By jointly modeling section hierarchy and domain lexicon signals, FinGEAR improves retrieval fidelity and provides a practical foundation for high-stakes financial analysis.","short_abstract":"Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval f...","url_abs":"https://arxiv.org/abs/2509.12042","url_pdf":"https://arxiv.org/pdf/2509.12042v1","authors":"[\"Ying Li\",\"Mengyu Wang\",\"Miguel de Carvalho\",\"Sotirios Sabanis\",\"Tiejun Ma\"]","published":"2025-09-15T15:25:26Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.CL\"]","methods":"[\"RAG\"]","has_code":false}