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A. Hernández-Cano, A. Hägele, A. Hao Huang, A. Romanou, A. Solergibert, B. Pásztor, B. Messmer, D. Garbaya, E. Frank Ďurech, I. Hakimi, J. Garcia Giraldo, M. Ismayilzada, N. Foroutan, S. Moalla, T. Chen, V. Sabolčec, Y. Xu, M. Aerni, B. AlKhamissi, I. Altemir Marinas, M. Hossein Amani, M. Ansaripour, I. Badanin, H. Benoit, E. Boros, N. John Browning, F. Bösch, M. Böther, N. Canova, C. Challier, C. Charmillot, J. Coles, J. Milan Deriu, A. Devos, L. Drescher, D. Dzenhaliou, M. Ehrmann, D. Fan, S. Fan, S. Gao, M. Gila, M. Grandury, D. Hashemi, A. Miserlis Hoyle, J. Jiang, M. Klein, A. Kucharavy, A. Kucherenko, F. Lübeck, R. Machacek, T. Ioannis Manitaras, A. Marfurt, K. Matoba, S. Matrenok, H. Mendonça, F. Roberto Mohamed, S. Montariol, L. Mouchel, S. Najem-Meyer, J. Ni, G. Oliva, M. Pagliardini, E. Palme, A. Panferov, L. Paoletti, M. Passerini, I. Pavlov, A. Poiroux, K. Ponkshe, N. Ranchin, J. Rando, M. Sauser, J. Saydaliev, M. Sayfiddinov, M. Schneider, S. Schuppli, M. Scialanga, A. Semenov, K. Shridhar, R. Singhal, A. Sotnikova, A. Sternfeld, A. Kumar Tarun, P. Teiletche, J. Vamvas, X. Yao, H. Zhao, A. Ilic, A. Klimovic, A. Krause, C. Gulcehre, D. Rosenthal, E. Ash, F. Tramèr, J. VandeVondele, L. Veraldi, M. Rajman, T. C. Schulthess, T. Hoefler, A. Bosselut, M. Jaggi, I. Schlag:

 Apertus: Democratizing Open and Compliant LLMs for Global Language Environments

(In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), presented in San Diego, California, United States, pages 46877-46955, Association for Computational Linguistics, ISBN: 979-8-89176-390-6, Jul. 2026)

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Abstract

Open LLMs enable AI practitioners to control development costs by building on an existing foundation for downstream applications. While offering substantial promise, current models often fail to meet the needs of users needing open solutions aligned with responsible AI principles, including data compliance, transparency, and inclusivity. In this work, we present Apertus, a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today's open model ecosystem, namely data responsibility and global representation. Unlike many prior models that release weights without reproducible data pipelines or regard for content-owner rights, Apertus models are pretrained exclusively on openly available data, retroactively respecting robots.txt exclusions and filtering for non-permissive, toxic, and personally identifiable content. To mitigate risks of data memorization, we also adopt the Goldfish objective during pretraining, strongly suppressing verbatim recall of data while retaining downstream task performance. Apertus also drastically expands multilingual coverage, training on 15T tokens from over approximately 1800 languages, with about 40% of pretraining data allocated to non-English content. Released at 8B and 70B scales, Apertus approaches state-of-the-art results among fully open models on multilingual benchmarks, rivaling or surpassing open-weight counterparts.

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BibTeX

@inproceedings{hernandez-cano-etal-2026-apertus,
  author={Alejandro Hernández-Cano and Alexander Hägele and Allen Hao Huang and Angelika Romanou and Antoni-Joan Solergibert and Barna Pásztor and Bettina Messmer and Dhia Garbaya and Eduard Frank Ďurech and Ido Hakimi and Juan Garcia Giraldo and Mete Ismayilzada and Negar Foroutan and Skander Moalla and Tiancheng Chen and Vinko Sabolčec and Yixuan Xu and Michael Aerni and Badr AlKhamissi and Inés Altemir Marinas and Mohammad Hossein Amani and Matin Ansaripour and Ilia Badanin and Harold Benoit and Emanuela Boros and Nicholas John Browning and Fabian Bösch and Maximilian Böther and Niklas Canova and Camille Challier and Clément Charmillot and Jonathan Coles and Jan Milan Deriu and Arnout Devos and Lukas Drescher and Daniil Dzenhaliou and Maud Ehrmann and Dongyang Fan and Simin Fan and Silin Gao and Miguel Gila and María Grandury and Diba Hashemi and Alexander Miserlis Hoyle and Jiaming Jiang and Mark Klein and Andrei Kucharavy and Anastasiia Kucherenko and Frederike Lübeck and Roman Machacek and Theofilos Ioannis Manitaras and Andreas Marfurt and Kyle Matoba and Simon Matrenok and Henrique Mendonça and Fawzi Roberto Mohamed and Syrielle Montariol and Luca Mouchel and Sven Najem-Meyer and Jingwei Ni and Gennaro Oliva and Matteo Pagliardini and Elia Palme and Andrei Panferov and Léo Paoletti and Marco Passerini and Ivan Pavlov and Auguste Poiroux and Kaustubh Ponkshe and Nathan Ranchin and Javier Rando and Mathieu Sauser and Jakhongir Saydaliev and Mukhammadali Sayfiddinov and Marian Schneider and Stefano Schuppli and Marco Scialanga and Andrei Semenov and Kumar Shridhar and Raghav Singhal and Anna Sotnikova and Alexander Sternfeld and Ayush Kumar Tarun and Paul Teiletche and Jannis Vamvas and Xiaozhe Yao and Hao Zhao and Alexander Ilic and Ana Klimovic and Andreas Krause and Caglar Gulcehre and David Rosenthal and Elliott Ash and Florian Tramèr and Joost VandeVondele and Livio Veraldi and Martin Rajman and Thomas C. Schulthess and Torsten Hoefler and Antoine Bosselut and Martin Jaggi and Imanol Schlag},
  title={{Apertus: Democratizing Open and Compliant LLMs for Global Language Environments}},
  year={2026},
  month={07},
  pages={46877-46955},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  location={San Diego, California, United States},
  publisher={Association for Computational Linguistics},
  isbn={979-8-89176-390-6},
  doi={10.18653/v1/2026.acl-long.2172},
}