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cs.CL2026
Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM
Adam Zachary Wasserman, David Beauchemin
We submit MéTRON-FR, a 125M GPT-2 pretrained on 92.47M words of French, to the BabyLM 2026 Strict track. It scores 85.97 +/- 0.17% on QFrBLiMP (a native Quebec-French benchmark of…
cs.HC2026
A Human-Centric Evaluation of a Retrieval-Augmented Generation System for Explaining Quebec Insurance Contracts
David Beauchemin, Richard Khoury
With the rise of online insurance sales, consumers face a significant \enquote{advice gap}, requiring them to navigate complex legal contracts without expert guidance. This paper p…
cs.CL2026
Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation
David Beauchemin, Richard Khoury
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…