Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
Correct Metadata for
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.- Anthology ID:
- 2026.acl-long.2172
- Volume:
- Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
- Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 46877–46955
- Language:
- URL:
- https://aclanthology.org/2026.acl-long.2172/
- DOI:
- Bibkey:
- Cite (ACL):
- Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, and Imanol Schlag. 2026. 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), pages 46877–46955, San Diego, California, United States. Association for Computational Linguistics.
- Cite (Informal):
- Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (Hernández-Cano et al., ACL 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.acl-long.2172.pdf
- Checklist:
- 2026.acl-long.2172.checklist.pdf
Export citation
@inproceedings{hernandez-cano-etal-2026-apertus,
title = "Apertus: Democratizing Open and Compliant {LLM}s for Global Language Environments",
author = {Hern{\'a}ndez-Cano, Alejandro and
H{\"a}gele, Alexander and
Huang, Allen Hao and
Romanou, Angelika and
Solergibert, Antoni-Joan and
P{\'a}sztor, Barna and
Messmer, Bettina and
Garbaya, Dhia and
{\v{D}}urech, Eduard Frank and
Hakimi, Ido and
Giraldo, Juan Garcia and
Ismayilzada, Mete and
Foroutan, Negar and
Moalla, Skander and
Chen, Tiancheng and
Sabol{\v{c}}ec, Vinko and
Xu, Yixuan and
Aerni, Michael and
AlKhamissi, Badr and
Marinas, In{\'e}s Altemir and
Amani, Mohammad Hossein and
Ansaripour, Matin and
Badanin, Ilia and
Benoit, Harold and
Boros, Emanuela and
Browning, Nicholas John and
B{\"o}sch, Fabian and
B{\"o}ther, Maximilian and
Canova, Niklas and
Challier, Camille and
Charmillot, Cl{\'e}ment and
Coles, Jonathan and
Deriu, Jan Milan and
Devos, Arnout and
Drescher, Lukas and
Dzenhaliou, Daniil and
Ehrmann, Maud and
Fan, Dongyang and
Fan, Simin and
Gao, Silin and
Gila, Miguel and
Grandury, Mar{\'i}a and
Hashemi, Diba and
Hoyle, Alexander Miserlis and
Jiang, Jiaming and
Klein, Mark and
Kucharavy, Andrei and
Kucherenko, Anastasiia and
L{\"u}beck, Frederike and
Machacek, Roman and
Manitaras, Theofilos Ioannis and
Marfurt, Andreas and
Matoba, Kyle and
Matrenok, Simon and
Mendon{\c{c}}a, Henrique and
Mohamed, Fawzi Roberto and
Montariol, Syrielle and
Mouchel, Luca and
Najem-Meyer, Sven and
Ni, Jingwei and
Oliva, Gennaro and
Pagliardini, Matteo and
Palme, Elia and
Panferov, Andrei and
Paoletti, L{\'e}o and
Passerini, Marco and
Pavlov, Ivan and
Poiroux, Auguste and
Ponkshe, Kaustubh and
Ranchin, Nathan and
Rando, Javier and
Sauser, Mathieu and
Saydaliev, Jakhongir and
Sayfiddinov, Mukhammadali and
Schneider, Marian and
Schuppli, Stefano and
Scialanga, Marco and
Semenov, Andrei and
Shridhar, Kumar and
Singhal, Raghav and
Sotnikova, Anna and
Sternfeld, Alexander and
Tarun, Ayush Kumar and
Teiletche, Paul and
Vamvas, Jannis and
Yao, Xiaozhe and
Zhao, Hao and
Ilic, Alexander and
Klimovic, Ana and
Krause, Andreas and
Gulcehre, Caglar and
Rosenthal, David and
Ash, Elliott and
Tram{\`e}r, Florian and
VandeVondele, Joost and
Veraldi, Livio and
Rajman, Martin and
Schulthess, Thomas C. and
Hoefler, Torsten and
Bosselut, Antoine and
Jaggi, Martin and
Schlag, Imanol},
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2172/",
pages = "46877--46955",
ISBN = "979-8-89176-390-6",
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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<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.</abstract>
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%0 Conference Proceedings %T Apertus: Democratizing Open and Compliant LLMs for Global Language Environments %A Hernández-Cano, Alejandro %A Hägele, Alexander %A Huang, Allen Hao %A Romanou, Angelika %A Solergibert, Antoni-Joan %A Pásztor, Barna %A Messmer, Bettina %A Garbaya, Dhia %A Ďurech, Eduard Frank %A Hakimi, Ido %A Giraldo, Juan Garcia %A Ismayilzada, Mete %A Foroutan, Negar %A Moalla, Skander %A Chen, Tiancheng %A Sabolčec, Vinko %A Xu, Yixuan %A Aerni, Michael %A AlKhamissi, Badr %A Marinas, Inés Altemir %A Amani, Mohammad Hossein %A Ansaripour, Matin %A Badanin, Ilia %A Benoit, Harold %A Boros, Emanuela %A Browning, Nicholas John %A Bösch, Fabian %A Böther, Maximilian %A Canova, Niklas %A Challier, Camille %A Charmillot, Clément %A Coles, Jonathan %A Deriu, Jan Milan %A Devos, Arnout %A Drescher, Lukas %A Dzenhaliou, Daniil %A Ehrmann, Maud %A Fan, Dongyang %A Fan, Simin %A Gao, Silin %A Gila, Miguel %A Grandury, María %A Hashemi, Diba %A Hoyle, Alexander Miserlis %A Jiang, Jiaming %A Klein, Mark %A Kucharavy, Andrei %A Kucherenko, Anastasiia %A Lübeck, Frederike %A Machacek, Roman %A Manitaras, Theofilos Ioannis %A Marfurt, Andreas %A Matoba, Kyle %A Matrenok, Simon %A Mendonça, Henrique %A Mohamed, Fawzi Roberto %A Montariol, Syrielle %A Mouchel, Luca %A Najem-Meyer, Sven %A Ni, Jingwei %A Oliva, Gennaro %A Pagliardini, Matteo %A Palme, Elia %A Panferov, Andrei %A Paoletti, Léo %A Passerini, Marco %A Pavlov, Ivan %A Poiroux, Auguste %A Ponkshe, Kaustubh %A Ranchin, Nathan %A Rando, Javier %A Sauser, Mathieu %A Saydaliev, Jakhongir %A Sayfiddinov, Mukhammadali %A Schneider, Marian %A Schuppli, Stefano %A Scialanga, Marco %A Semenov, Andrei %A Shridhar, Kumar %A Singhal, Raghav %A Sotnikova, Anna %A Sternfeld, Alexander %A Tarun, Ayush Kumar %A Teiletche, Paul %A Vamvas, Jannis %A Yao, Xiaozhe %A Zhao, Hao %A Ilic, Alexander %A Klimovic, Ana %A Krause, Andreas %A Gulcehre, Caglar %A Rosenthal, David %A Ash, Elliott %A Tramèr, Florian %A VandeVondele, Joost %A Veraldi, Livio %A Rajman, Martin %A Schulthess, Thomas C. %A Hoefler, Torsten %A Bosselut, Antoine %A Jaggi, Martin %A Schlag, Imanol %Y Liakata, Maria %Y Moreira, Viviane P. %Y Zhang, Jiajun %Y Jurgens, David %S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) %D 2026 %8 July %I Association for Computational Linguistics %C San Diego, California, United States %@ 979-8-89176-390-6 %F hernandez-cano-etal-2026-apertus %X 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. %U https://aclanthology.org/2026.acl-long.2172/ %P 46877-46955
Markdown (Informal)
[Apertus: Democratizing Open and Compliant LLMs for Global Language Environments](https://aclanthology.org/2026.acl-long.2172/) (Hernández-Cano et al., ACL 2026)
- Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (Hernández-Cano et al., ACL 2026)
ACL
- Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, and Imanol Schlag. 2026. 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), pages 46877–46955, San Diego, California, United States. Association for Computational Linguistics.