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Second, we propose a calibrated refusal mechanism: training on 22% unanswerable examples yield a 12% \"I don't know\" rate, substantially improving over the base model's unsafe 4.3% rate","title":"FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking","url":"https://arxiv.org/abs/2605.05482","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.05482v2 Announce Type: replace \nAbstract: Large language models (LLMs) are rapidly being adopted across various domains. However, their adoption in banking industry faces resistance due to demands for high accuracy, regulatory compliance, and the need for verifiable and grounded responses. We present a unified, data-efficient framework for training grounded domain-specific LLMs that optimizes answer quality, citation grounding, and calibrated refusal under real-world deployment constraints. 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