Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation

ArXi:2603.07825v1 Announce Type: new The digitization of insurance distribution in the Canadian province of Quebec, accelerated by legislative changes such as Bill 141, has created a significant "advice gap", leaving consumers to interpret complex financial contracts without professional guidance. While Large Language Models (LLMs) offer a scalable solution for automated advisory services, their deployment in high-stakes domains hinges on strict legal accuracy and trustworthiness. In this paper, we address this challenge by.