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    <abstract>Financial disclosures such as 10-K filings pose challenging retrieval problems because of their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We present 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 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.</abstract>
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%0 Conference Proceedings
%T FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval
%A Li, Ying
%A Wang, Mengyu
%A de Carvalho, Miguel
%A Sabanis, Sotirios
%A Ma, Tiejun
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-fingear
%X Financial disclosures such as 10-K filings pose challenging retrieval problems because of their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We present 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 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.
%R 10.18653/v1/2025.findings-emnlp.382
%U https://aclanthology.org/2025.findings-emnlp.382/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.382
%P 7239-7255

Markdown (Informal)

[FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval](https://aclanthology.org/2025.findings-emnlp.382/) (Li et al., Findings 2025)

• FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval (Li et al., Findings 2025)

ACL

• Ying Li, Mengyu Wang, Miguel de Carvalho, Sotirios Sabanis, and Tiejun Ma. 2025. FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7239–7255, Suzhou, China. Association for Computational Linguistics.