Evidence-Gated Bilingual RAG for Reliable Consumer Banking Search
This paper presents an evidence-gated bilingual retrieval-augmented generation (RAG) framework for consumer banking search that operates entirely on local infrastructure.
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Key points
- 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 problem is harder in bilingual services, where the same intent may be expressed in Arabic or English.
- The framework combines structured web-content refinement, hybrid dense-lexical retrieval, intent-aware evidence selection, cross-encoder reranking, and a three-outcome Answer / Related Mismatch / No Answer decision mechanism.
- On 210 URL-labeled Arabic and English queries, the proposed approach improves Top-1 hit rate from 50.48% to 79.05% and MRR@5 from 0.571 to 0.797 over a hybrid baseline.
Sources (1)
- [1]Evidence-Gated Bilingual RAG for Reliable Consumer Banking SearcharXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:12 PM
This paper presents an evidence-gated bilingual retrieval-augmented generation (RAG) framework for consumer banking search that operates entirely on local infrastructure.
Consumers often describe what they need rather than use the terminology found on a banking website.
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