wachsmuth

Kilsbach S, Rezat S, Michel N, Karabey R, Stahl M, Wachsmuth H. Mehrebenenannotation argumentativer Lerner∗innentexte für die automatische Textauswertung. Zeitschrift fur Angewandte Linguistik. 2025 Mär 13;2025(82):102–129. doi: 10.1515/zfal-2025-2003

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Spliethöver M, Knebler T, Fumagalli F, Muschalik M, Hammer B, Hüllermeier E et al. Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. in Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies. Band 1. Albuquerque, New Mexico: Association for Computational Linguistics. 2025

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Alshomary M, Lange F, Booshehri M, Sengupta M, Cimiano P, Wachsmuth H. Modeling the Quality of Dialogical Explanations. in Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, Hrsg., 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings. European Language Resources Association (ELRA). 2024. S. 11523-11536. (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings). doi: 10.48550/arXiv.2403.00662

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Chen WF, Alshomary M, Stahl M, Al Khatib K, Stein B, Wachsmuth H. Reference-guided Style-Consistent Content Transfer. in Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, Hrsg., 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings. European Language Resources Association (ELRA). 2024. S. 13754-13768

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El Baff R, Khatib KA, Alshomary M, Konen K, Stein B, Wachsmuth H. Improving Argument Effectiveness Across Ideologies using Instruction-tuned Large Language Models. in Al-Onaizan Y, Bansal M, Chen YN, Hrsg., Findings of the Association for Computational Linguistics: EMNLP 2024. Miami, Florida, USA: Association for Computational Linguistics. 2024. S. 4604-4622 doi: 10.18653/v1/2024.findings-emnlp.265

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Faggioli G, Dietz L, Clarke CLA, Demartini G, Hagen M, Hauff C et al. Who Determines What Is Relevant? Humans or AI? Why Not Both? A spectrum of human–artificial intelligence collaboration in assessing relevance. Communications of the ACM. 2024 Mär 25;67(4):31-34. Epub 2024 Mär 15. doi: 10.1145/3624730

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Feldhus N, Anagnostopoulou A, Wang Q, Alshomary M, Wachsmuth H, Sonntag D et al. Towards Modeling and Evaluating Instructional Explanations in Teacher-Student Dialogues. in Proceedings of the 2024 International Conference on Information Technology for Social Good. New York, NY, USA: Association for Computing Machinery. 2024. S. 225–230 doi: 10.1145/3677525.3678665

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Kiesel J, Çöltekin Ç, Heinrich M, Fröbe M, Alshomary M, De Longueville B et al. Overview of Touché 2024: Argumentation Systems. in Goharian N, Tonellotto N, He Y, Lipani A, McDonald G, Macdonald C, Ounis I, Hrsg., Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. S. 466-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-56069-9_64

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Mirzakhmedova N, Kiesel J, Alshomary M, Heinrich M, Handke N, Cai X et al. The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. in Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, Hrsg., Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino, Italia: ELRA and ICCL. 2024. S. 16121-16134

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Scharlau I, Körber M, Sengupta M, Wachsmuth H. When to use a metaphor: Metaphors in dialogical explanations with addressees of different expertise. Frontiers in Language Sciences. 2024 Dez 18;3:1474924. doi: 10.3389/flang.2024.1474924

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Sengupta M, El Baff R, Alshomary M, Wachsmuth H. Analyzing the Use of Metaphors in News Editorials for Political Framing. in Duh K, Gomez H, Bethard S, Hrsg., Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Mexico City, Mexico: Association for Computational Linguistics (ACL). 2024. S. 3621–3631. (Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024). doi: 10.18653/v1/2024.naacl-long.199

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Spliethöver M, Menon SN, Wachsmuth H. Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. in Ku LW, Martins A, Srikumar V, Hrsg., Findings of the Association for Computational Linguistics ACL 2024. 2024. S. 9294-9313. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2024.findings-acl.553

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Stahl M, Michel N, Kilsbach S, Schmidtke J, Rezat S, Wachsmuth H. A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality. in Duh K, Gomez H, Bethard S, Hrsg., Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. S. 2661–2674 doi: 10.18653/v1/2024.naacl-long.145, 10.48550/arXiv.2404.02529

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Stahl M, Biermann L, Nehring A, Wachsmuth H. Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation. in Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024). 2024. S. 283–298

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Tornede A, Deng D, Eimer T, Giovanelli J, Mohan A, Ruhkopf T et al. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. 2024 Feb 9. Epub 2024 Feb 9. doi: 10.48550/arXiv.2306.08107

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Wachsmuth H, Lapesa G, Cabrio E, Lauscher A, Park J, Vecchi EM et al. Argument Quality Assessment in the Age of Instruction-Following Large Language Models. in Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, Hrsg., Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL. 2024. S. 1519-1538 doi: 10.48550/arXiv.2403.16084

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Ziegenbein T, Skitalinska G, Bayat Makou A, Wachsmuth H. LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback. in Ku LW, Martins AFT, Srikumar V, Hrsg., Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Band 1. Association for Computational Linguistics (ACL). 2024. S. 4455-4476. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.48550/arXiv.2406.03363, 10.18653/v1/2024.acl-long.244

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Ziegenbein T, Syed S, Potthast M, Wachsmuth H. Objective Argument Summarization in Search. in Cimiano P, Frank A, Kohlhase M, Stein B, Hrsg., Robust Argumentation Machines - First International Conference, RATIO 2024, Proceedings: First International Conference, RATIO 2024, Bielefeld, Germany, June 5–7, 2024, Proceedings. 1. Aufl. Cham: Springer. 2024. S. 335-351. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-63536-6_20

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Alshomary M, Wachsmuth H. Conclusion-based Counter-Argument Generation. in EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2023. S. 957-967 Epub 2023 Jan 24. doi: 10.48550/arXiv.2301.09911

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Bäumer F, Chen WF, Geierhos M, Kersting J, Wachsmuth H. Dialogue-Based Requirement Compensation and Style-Adjusted Data-To-Text Generation. in On-The-Fly Computing : Individualized IT-Services in dynamic markets. 2023. S. 65-84 doi: 10.5281/zenodo.8068456

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Henning Wachsmuth

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