Papers by Roman Schneider
CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering (2023.emnlp-industry)
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Md Rashad Al Hasan Rony, Christian Suess, Sinchana Ramakanth Bhat, Viju Sudhi, Julia Schneider, Maximilian Vogel, Roman Teucher, Ken Friedl, Soumya Sahoo
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance by following natural language instructions without fine-tuning them on domain-specific tasks and data. |
| Approach: | They propose an in-car retrieval-augmented conversational question-answering system that uses large language models to generate natural, safe and domain-specific answers. |
| Outcome: | The proposed system outperforms state-of-the-art LLMs in generating safe and domain-specific answers. |
GeCoTagger: Annotation of German Verb Complements with Conditional Random Fields (L18-1)
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| Challenge: | Complement phrases are essential for constructing well-formed sentences in German. |
| Approach: | They propose an algorithm which can identify and classify complement phrases of any German verb in any written sentence context. |
| Outcome: | The proposed algorithm can identify and classify complement phrases of any German verb in any written sentence context. |
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. |
A Corpus Linguistic Perspective on Contemporary German Pop Lyrics with the Multi-Layer Annotated “Songkorpus” (2020.lrec-1)
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| Challenge: | TEI-compliant song lyrics are used as primary data, linguistically and literary motivated annotations, and extralinguistic metadata. |
| Approach: | They propose to annotate a multiply annotated corpus of German lyrics as a publicly available basis for multidisciplinary research. |
| Outcome: | The proposed corpus of german lyrics is available for evaluation and analysis using TEI-compliant, linguistically and literary motivated annotations and extralinguistic metadata. |