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New RAG framework boosts bilingual banking search accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an evidence-gated bilingual retrieval-augmented generation (RAG) framework designed to improve search accuracy in consumer banking, particularly for bilingual services. This system combines structured web content refinement, hybrid dense-lexical retrieval, and intent-aware evidence selection to better match natural language queries with relevant banking information. Preliminary results show a significant improvement in Top-1 hit rate and MRR@5 compared to a hybrid baseline, with a high percentage of generated answers supported by a faithfulness judge. AI

IMPACT This framework could improve the accuracy and user experience of search functions in bilingual consumer banking applications.

RANK_REASON The item is a research paper detailing a new framework for retrieval-augmented generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RAG framework boosts bilingual banking search accuracy

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The item is a research paper detailing a new framework for retrieval-augmented generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ahmed Mohamed Mahmoud Rayyan, Rana Mahmoud Mohammed, Aya Abdallah Abdelmonem, Mohamed Khaled Goda Othman, Rahatara Ferdousi ·

    Evidence-Gated Bilingual RAG for Reliable Consumer Banking Search

    arXiv:2610.22652v1 Announce Type: new Abstract: Consumers often describe what they need rather than use the terminology found on a banking website. For example, a natural-language request ("a card for travel") may not share the keywords of the relevant product page, and the probl…

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