Search (11 results, page 1 of 1)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"el"
  • × year_i:[2020 TO 2030}
  1. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.01 
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    Date
    29.12.2022 18:22:55
    Source
    c't. 2023, H.1, S.46- [https://www.heise.de/select/ct/2023/1/2233908274346530870]
  2. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.01 
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    Date
    29. 3.2023 13:23:26
    29. 3.2023 13:29:19
  3. Rieger, F.: Lügende Computer (2023) 0.00 
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    Date
    16. 3.2023 19:22:55
  4. Donath, A.: Nutzungsverbote für ChatGPT (2023) 0.00 
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    Content
    Milliardenbewertung für ChatGPT OpenAI, das Chatbot ChatGPT betreibt, befindet sich laut einem Bericht des Wall Street Journals in Gesprächen zu einem Aktienverkauf. Das WSJ meldete, der mögliche Verkauf der Aktien würde die Bewertung von OpenAI auf 29 Milliarden US-Dollar anheben. Sorgen auch in Brandenburg Der brandenburgische SPD-Abgeordnete Erik Stohn stellte mit Hilfe von ChatGPT eine Kleine Anfrage an den Brandenburger Landtag, in der er fragte, wie die Landesregierung sicherstelle, dass Studierende bei maschinell erstellten Texten gerecht beurteilt und benotet würden. Er fragte auch nach Maßnahmen, die ergriffen worden seien, um sicherzustellen, dass maschinell erstellte Texte nicht in betrügerischer Weise von Studierenden bei der Bewertung von Studienleistungen verwendet werden könnten.
  5. Lutz-Westphal, B.: ChatGPT und der "Faktor Mensch" im schulischen Mathematikunterricht (2023) 0.00 
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    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.19-21
  6. Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023) 0.00 
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  7. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023) 0.00 
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    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.17-19
  8. Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020) 0.00 
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    Abstract
    Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
  9. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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    Pages
    S.169-185
  10. Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.00 
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  11. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022) 0.00 
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    Content
    Vgl. auch den Abdruck des Beitrages in: Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.17-19. Vgl. auch ihr Video bei Youtube: https://www.youtube.com/watch?v=cMuBo_rH15c.

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