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The paper introduces compar:IA, an open-source digital public service by the French government designed to collect large-scale, French-language human preference data for LLMs using a blind pairwise comparison interface. This addresses the scarcity of non-English preference data needed for training methods like RLHF and DPO, which leads to reduced performance in non-English languages. The platform has collected over 600,000 prompts and 250,000 preference votes, predominantly in French, and the authors release three datasets (conversations, votes, reactions) under open licenses, along with initial analyses and a French-language model leaderboard.
The French government built an open-source platform that's already collected a massive, openly-licensed dataset of French language prompts and preferences, offering a critical resource for improving LLMs in languages beyond English.
Large Language Models (LLMs) often show reduced performance, cultural alignment, and safety robustness in non-English languages, partly because English dominates both pre-training data and human preference alignment datasets. Training methods like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) require human preference data, which remains scarce and largely non-public for many languages beyond English. To address this gap, we introduce compar:IA, an open-source digital public service developed inside the French government and designed to collect large-scale human preference data from a predominantly French-speaking general audience. The platform uses a blind pairwise comparison interface to capture unconstrained, real-world prompts and user judgments across a diverse set of language models, while maintaining low participation friction and privacy-preserving automated filtering. As of 2026-02-07, compar:IA has collected over 600,000 free-form prompts and 250,000 preference votes, with approximately 89% of the data in French. We release three complementary datasets -- conversations, votes, and reactions -- under open licenses, and present initial analyses, including a French-language model leaderboard and user interaction patterns. Beyond the French context, compar:IA is evolving toward an international digital public good, offering reusable infrastructure for multilingual model training, evaluation, and the study of human-AI interaction.
