Henning Wachsmuth
Prof. Dr. rer. nat. Henning Wachsmuth
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Welfengarten 1
30167 Hannover
30167 Hannover
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Prof. Dr. rer. nat. Henning Wachsmuth
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Publications
Showing results 1 - 20 out of 139
Ajjour Y, Wachsmuth H. Exploring LLM Priming Strategies for Few-Shot Stance Classification. In Chistova E, Cimiano P, Haddadan S, Lapesa G, Ruiz-Dolz R, editors, Proceedings of the 12th Argument Mining Workshop. Vienna, Austria: Association for Computational Linguistics. 2025. p. 11-23 doi: 10.18653/v1/2025.argmining-1.2
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Anagnostopoulou A, Feldhus N, Hsu YS, Alshomary M, Wachsmuth H, Sonntag D. Human and LLM-based Assessment of Teaching Acts in Expert-led Explanatory Dialogues. In Strube M, Braud C, Hardmeier C, Li JJ, Loaiciga S, Zeldes A, Li C, editors, Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025). Suzhou, China: Association for Computational Linguistics. 2025. p. 166-181 doi: 10.18653/v1/2025.codi-1.15
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Chen MH, Chen WF, Mudgal G, Wachsmuth H. Cross-Cultural Comparison of Argument Structures Among English Learners: Argument Proficiency, Patterns, and Communication Styles. ARGUMENTATION. 2025 Dec;39(4):571-599. Epub 2025 Aug 12. doi: 10.1007/s10503-025-09670-3
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Fehring L, Wever M, Spliethöver M, Hennig L, Wachsmuth H, Lindauer M. Towards Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization. In Workshop Track of the AutoML Conference . 2025
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Fichtel L, Spliethöver M, Hüllermeier E, Jimenez P, Klowait N, Kopp S et al. Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues. In Béchet F, Lefèvre F, Asher N, Kim S, Merlin T, editors, Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Avignon, France: Association for Computational Linguistics. 2025. p. 1-20
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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 Mar 13;82(1):102–129. doi: 10.1515/zfal-2025-2003
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Musi E, Kökciyan N, Khatib KA, Ceolin D, Dietz E, Gutekunst KM et al. Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design. In Chistova E, Cimiano P, Haddadan S, Lapesa G, Ruiz-Dolz R, editors, Proceedings of the 12th Argument Mining Workshop. Vienna, Austria: Association for Computational Linguistics. 2025. p. 24-31 doi: 10.18653/v1/2025.argmining-1.3
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Rezat S, Kilsbach S, Karabey R, Michel N, Stahl M, Wachsmuth H. Didaktische Modellierung automatisierten adaptiven Feedbacks zu argumentativen Lerner* innentexten. Leseräume: Zeitschrift für Literalität in Schule und Forschung. 2025;12(11).
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Romberg J, Maurer M, Wachsmuth H, Lapesa G. Towards a Perspectivist Turn in Argument Quality Assessment. In Chiruzzo L, Ritter A, Wang L, editors, Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics (ACL). 2025. p. 7458-7485. (Long Papers). doi: 10.18653/v1/2025.naacl-long.382
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Sengupta M, Muschalik M, Fumagalli F, Hammer B, Hüllermeier E, Ghosh D et al. Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions. In Christodoulopoulos C, Chakraborty T, Rose C, Peng V, editors, Findings of the Association for Computational Linguistics: EMNLP 2025. Suzhou, China: Association for Computational Linguistics. 2025. p. 17393-17403 doi: 10.18653/v1/2025.findings-emnlp.942
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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. Vol. 1. Albuquerque, New Mexico: Association for Computational Linguistics. 2025
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Stahl M, Ziegenbein T, Park J, Wachsmuth H. ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation. In Che W, Nabende J, Shutova E, Pilehvar MT, editors, Findings of the Association for Computational Linguistics: ACL 2025. 2025. p. 11103–11127. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2025.findings-acl.579
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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, editors, 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings. European Language Resources Association (ELRA). 2024. p. 11523-11536 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, editors, 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings. European Language Resources Association (ELRA). 2024. p. 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, editors, Findings of the Association for Computational Linguistics: EMNLP 2024. Miami, Florida, USA: Association for Computational Linguistics. 2024. p. 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 Mar 25;67(4):31-34. Epub 2024 Mar 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. p. 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, editors, Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Proceedings. Springer Science and Business Media Deutschland GmbH. 2024. p. 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, editors, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). Torino, Italia: ELRA and ICCL. 2024. p. 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 Dec 18;3:1474924. doi: 10.3389/flang.2024.1474924
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