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• [4]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2985109

  Bunzeck, B., Zarrieß, S.: GPT-wee: How Small Can a Small Language Model Really Get? In: Warstadt, A., Mueller, A., Choshen, L., Wilcox, E., Zhuang, C., Ciro, J., Mosquera, R., Paranjabe, B., Williams, A., Linzen, T., and Cotterell, R. (eds.) Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning. p. 35-46. Association for Computational Linguistics, Stroudsburg, PA (2023).

  PUB | PDF | DOI | Download (ext.)
   
• [3]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980943

  Druskat, S., Krause, T., Lachenmaier, C., Bunzeck, B.: Hexatomic: An extensible, OS-independent platform fordeep multi-layer linguistic annotation of corpora. Journal of Open Source Software. 8, : 4825 (2023).

  PUB | PDF | DOI
   
• [2]
  

  2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2980942

  Wojcik, P., Bunzeck, B., Zarrieß, S.: The Wikipedia Republic of Literary Characters. Journal of Cultural Analytics. 8, (2023).

  PUB | PDF | DOI
   
• [1]
  

  2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982902

  Bunzeck, B., Zarrieß, S.: Entrenchment Matters: Investigating Positional and Constructional Sensitivity in Small and Large Language Models. In: Breitholtz, E., Lappin, S., Loaiciga, S., Ilinykh, N., and Dobnik, S. (eds.) Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD). p. 25-37. Association for Computational Linguistics, Stroudsburg, PA (2023).

  PUB | PDF