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• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985222

  Sieker, J.; Zarrieß, S. (2023): When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle. In: Yonatan Belinkov; Sophie Hao; Jaap Jumelet; Najoung Kim; Arya McCarthy; Hosein Mohebbi; Association for Computational Linguistics (Hrsg.): Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP. S. 180–198.

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• [3]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983376

  Sieker, J.; Bott, O.; Solstad, T.; Zarrieß, S. (2023): Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models. In: C. Maria Keet; Hung-Yi Lee; Sina Zarrieß; Association for Computational Linguistics (Hrsg.): Proceedings of the 16th International Natural Language Generation Conference. S. 206–220.

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• [2]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968165

  Sieker, J.; Solstad, T. (2022): Projective variability of (semi)factive verbs in family of sentence contexts: A rating study. In: Marco Degano; Tom Roberts; Giorgio Sbardolini; Marieke Schouwstra; Institute for Logic, Language and Computation (ILLC) (Hrsg.): Proceedings of the 23rd Amsterdam Colloquium . Amsterdam. S. 282-289.

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• [1]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2965490

  Reiter, N.; Sieker, J.; Guhr, S.; Gius, E.; Zarrieß, S. (2022): Exploring Text Recombination for Automatic Narrative Level Detection. In: Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022). European Language Resources Association (ELRA). S. 3346-3353.

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