Papers by Pablo Duboue

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Challenge:	Large language models (LLMs) are rapidly being adopted across various domains, but adoption in the regulated banking industry is limited due to their tendency to hallucinate, exhibit over-agreeable behavior, and lack alignment with domain-specific knowledge and constraints.
Approach:	They propose a framework for training grounded domain-specific LLMs that optimizes answer quality, citation grounding, and calibrated refusal under real-world deployment constraints.
Outcome:	The proposed model outperforms GPT-4.1 on citation grounding and calibrated refusal under real-world deployment constraints.