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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, et al., eds. Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning. Stroudsburg, PA: Association for Computational Linguistics; 2023: 35-46.

  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. 2023;8(86): 4825.

  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. 2023;8(2).

  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, Dobnik S, eds. Proceedings of the 2023 CLASP Conference on Learning with Small Data (LSD). Stroudsburg, PA: Association for Computational Linguistics; 2023: 25-37.

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