-
Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023)
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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]
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Rieger, F.: Lügende Computer (2023)
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- Date
- 16. 3.2023 19:22:55
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Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018)
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- Date
- 22. 1.2018 11:32:44
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Lutz-Westphal, B.: ChatGPT und der "Faktor Mensch" im schulischen Mathematikunterricht (2023)
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- Source
- Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.19-21
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Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023)
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-
Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023)
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- Source
- Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.17-19
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Stoykova, V.; Petkova, E.: Automatic extraction of mathematical terms for precalculus (2012)
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- Abstract
- In this work, we present the results of research for evaluating a methodology for extracting mathematical terms for precalculus using the techniques for semantically-oriented statistical search. We use the corpus-based approach and the combination of different statistically-based techniques for extracting keywords, collocations and co-occurrences incorporated in the Sketch Engine software. We evaluate the collocations candidate terms for the basic concept function(s) and approve the related methodology by precalculus domain conceptual terms definitions. Finally, we offer a conceptual terms hierarchical representation and discuss the results with respect to their possible applications.
- Source
- Procedia Technology. 1(2012), S.464-468
-
Rajasurya, S.; Muralidharan, T.; Devi, S.; Swamynathan, S.: Semantic information retrieval using ontology in university domain (2012)
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-
Zadeh, B.Q.; Handschuh, S.: ¬The ACL RD-TEC : a dataset for benchmarking terminology extraction and classification in computational linguistics (2014)
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- Pages
- S.52-63
-
Kiela, D.; Clark, S.: Detecting compositionality of multi-word expressions using nearest neighbours in vector space models (2013)
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-
Nielsen, R.D.; Ward, W.; Martin, J.H.; Palmer, M.: Extracting a representation from text for semantic analysis (2008)
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- Pages
- S.241-244
-
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)
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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.
-
Shen, M.; Liu, D.-R.; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies (2012)
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-
Bedathur, S.; Narang, A.: Mind your language : effects of spoken query formulation on retrieval effectiveness (2013)
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-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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- Pages
- S.169-185
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Bird, S.; Dale, R.; Dorr, B.; Gibson, B.; Joseph, M.; Kan, M.-Y.; Lee, D.; Powley, B.; Radev, D.; Tan, Y.F.: ¬The ACL Anthology Reference Corpus : a reference dataset for bibliographic research in computational linguistics (2008)
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Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022)
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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.
