-
Tay, A.: ¬The next generation discovery citation indexes : a review of the landscape in 2020 (2020)
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- Date
- 17.11.2020 12:22:59
-
DeSilva, J.M.; Traniello, J.F.A.; Claxton, A.G.; Fannin, L.D.: When and why did human brains decrease in size? : a new change-point analysis and insights from brain evolution in ants (2021)
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- Source
- Frontiers in ecology and evolution, 22 October 2021 [https://www.frontiersin.org/articles/10.3389/fevo.2021.742639/full]
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Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020)
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-
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.
-
Patriarca, S.: Information literacy gives us the tools to check sources and to verify factual statements : What does Popper`s "Es gibt keine Autoritäten" mean? (2021)
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Wolf, S.: Automating authority control processes (2020)
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Shiri, A.; Kelly, E.J.; Kenfield, A.; Woolcott, L.; Masood, K.; Muglia, C.; Thompson, S.: ¬A faceted conceptualization of digital object reuse in digital repositories (2020)
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Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo : a Web-Scale interface for ontology archiving under consumer-oriented aspects (2020)
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-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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- Pages
- S.169-185
-
Broughton, V.: Faceted classification in support of diversity : the role of concepts and terms in representing religion (2020)
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- Source
- ¬The Indexer: the international journal of indexing. 38(2020) no.3, S.247-270
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Daquino, M.; Peroni, S.; Shotton, D.; Colavizza, G.; Ghavimi, B.; Lauscher, A.; Mayr, P.; Romanello, M.; Zumstein, P.: ¬The OpenCitations Data Model (2020)
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Isaac, A.; Raemy, J.A.; Meijers, E.; Valk, S. De; Freire, N.: Metadata aggregation via linked data : results of the Europeana Common Culture project (2020)
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Franke, T.; Zoubir, M.: Technology for the people? : humanity as a compass for the digital transformation (2020)
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- Source
- Wirtschaftsdienst: Zeitschrift für Wirtschaftspolitik. 100(2020) H.13, S.4-11
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Dhillon, P.; Singh, M.: ¬An extended ontology model for trust evaluation using advanced hybrid ontology (2023)
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- Source
- Journal of information science. 41(2023) Jan., S.1-23
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Babcock, K.; Lee, S.; Rajakumar, J.; Wagner, A.: Providing access to digital collections (2020)
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Ogden, J.; Summers, E.; Walker, S.: Know(ing) Infrastructure : the wayback machine as object and instrument of digital research (2023)
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Singh, A.; Sinha, U.; Sharma, D.k.: Semantic Web and data visualization (2020)
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- Pages
- S.133-150
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Gil-Berrozpe, J.C.: Description, categorization, and representation of hyponymy in environmental terminology (2022)
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- 357 S
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Frey, J.; Streitmatter, D.; Götz, F.; Hellmann, S.; Arndt, N.: DBpedia Archivo (2020)
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Hofstadter, D.: Artificial neural networks today are not conscious, according to Douglas Hofstadter (2022)
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- Content
- Kommentar Autor: "I would call GPT-3's answers not just clueless but cluelessly clueless, meaning that GPT-3 has no idea that it has no idea about what it is saying. There are no concepts behind the GPT-3 scenes; rather, there's just an unimaginably huge amount of absorbed text upon which it draws to produce answers. But since it had no input text about, say, dropping things onto the Andromeda galaxy (an idea that clearly makes no sense), the system just starts babbling randomly-but it has no sense that its random babbling is random babbling. Much the same could be said for how it reacts to the absurd notion of transporting Egypt (for the second time) across the Golden Gate Bridge, or the idea of mile-high vases."
