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• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985222

  Sieker, J., Zarrieß, S.: When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle. In: Belinkov, Y., Hao, S., Jumelet, J., Kim, N., McCarthy, A., Mohebbi, H., and Association for Computational Linguistics (eds.) Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP. p. 180–198. (2023).

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• [3]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2983376

  Sieker, J., Bott, O., Solstad, T., Zarrieß, S.: Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models. In: Keet , C.M., Lee, H.-Y., Zarrieß, S., and Association for Computational Linguistics (eds.) Proceedings of the 16th International Natural Language Generation Conference. p. 206–220. (2023).

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• [2]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2968165

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

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• [1]
  

  2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2965490

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

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