Search (9 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) 0.03 
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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. Rieger, F.: Lügende Computer (2023) 0.01 
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    Date
    16. 3.2023 19:22:55
  3. 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
  4. Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023) 0.00 
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  5. 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
  6. 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.
  7. Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021) 0.00 
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    Pages
    S.169-185
  8. Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.00 
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  9. 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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