Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM
Paper recorded by Signals 4 on 2026-09-15 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-15 on arXiv · recorded by Signals 4 on 2026-09-16
Category: cs.CL · 自然语言处理 · first seen 2026-09-16
Abstract
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 grammatical minimal pairs) and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE (General Language Understanding Evaluation) protocol that combines French task-data translation with rank-16 LoRA (Low-Rank A
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Cite this page: Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM: the #27 most recent of 186 cs.CL papers we have recorded (as of 2026-09-15). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/right-tool-right-job-native-language-evaluation-tokenizer-sensitivity-and-method.html
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