Papers by Caglar Gulcehre
Self-Recognition in Language Models (2024.findings-emnlp)
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| Challenge: | Existing models for language modeling are very capable, but depend on commercial providers to build them. |
| Approach: | They propose a model-generated security question to assess self-recognition in LMs . they find no evidence of general or consistent self-reason in any examined LM . |
| Outcome: | The proposed approach can be externally administered to keep track of frontier models as it does not require access to internal model parameters or output probabilities. |
SIKeD: Self-guided Iterative Knowledge Distillation for Mathematical Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) can generate intermediate reasoning process for multistep reasoning tasks. |
| Approach: | They propose a distillation method that teaches the model to approach a task using different strategies and the model uses its self-generated on-policy outputs to choose the most suitable strategy. |
| Outcome: | The proposed method significantly outperforms distillation techniques on large models of different sizes. |
Aligning Large Language Models with Diverse Political Viewpoints (2024.emnlp-main)
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| Challenge: | Large language models such as ChatGPT exhibit striking political biases . a recent study shows that chatbots exhibit progressive, liberal, and proenvironmental biase . |
| Approach: | They propose to align large language models with 100,000 comments from candidates running for national parliament in Switzerland. |
| Outcome: | The proposed model generates more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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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
| Challenge: | Apertus is 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. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |