Zobrazeno 1 - 10
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pro vyhledávání: '"Kruschwitz, Udo"'
Autor:
Ateia, Samy, Kruschwitz, Udo
Commercial large language models (LLMs), like OpenAI's GPT-4 powering ChatGPT and Anthropic's Claude 3 Opus, have dominated natural language processing (NLP) benchmarks across different domains. New competing Open-Source alternatives like Mixtral 8x7
Externí odkaz:
http://arxiv.org/abs/2407.13511
Autor:
Her, Wan-Hua, Kruschwitz, Udo
Machine Translation has made impressive progress in recent years offering close to human-level performance on many languages, but studies have primarily focused on high-resource languages with broad online presence and resources. With the help of gro
Externí odkaz:
http://arxiv.org/abs/2404.08259
Autor:
Donabauer, Gregor, Kruschwitz, Udo
Pre-training of neural networks has recently revolutionized the field of Natural Language Processing (NLP) and has before demonstrated its effectiveness in computer vision. At the same time, advances around the detection of fake news were mainly driv
Externí odkaz:
http://arxiv.org/abs/2402.18179
Autor:
Ateia, Samy, Kruschwitz, Udo
We assessed the performance of commercial Large Language Models (LLMs) GPT-3.5-Turbo and GPT-4 on tasks from the 2023 BioASQ challenge. In Task 11b Phase B, which is focused on answer generation, both models demonstrated competitive abilities with le
Externí odkaz:
http://arxiv.org/abs/2306.16108
Autor:
Donabauer, Gregor, Kruschwitz, Udo
Fake news detection has become a research area that goes way beyond a purely academic interest as it has direct implications on our society as a whole. Recent advances have primarily focused on textbased approaches. However, it has become clear that
Externí odkaz:
http://arxiv.org/abs/2212.06560
Autor:
Yu, Juntao, Paun, Silviu, Camilleri, Maris, Garcia, Paloma Carretero, Chamberlain, Jon, Kruschwitz, Udo, Poesio, Massimo
Although several datasets annotated for anaphoric reference/coreference exist, even the largest such datasets have limitations in terms of size, range of domains, coverage of anaphoric phenomena, and size of documents included. Yet, the approaches pr
Externí odkaz:
http://arxiv.org/abs/2210.05581
Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside th
Externí odkaz:
http://arxiv.org/abs/2204.02712
Autor:
Hartl, Philipp, Kruschwitz, Udo
The distribution of fake news is not a new but a rapidly growing problem. The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to it of easy use the
Externí odkaz:
http://arxiv.org/abs/2204.01841
Autor:
Tran, Hoai Nam, Kruschwitz, Udo
Publikováno v:
In Proceedings of the GermEval 2021 Workshop on the Identification of Toxic, Engaging, and Fact-Claiming Comments: 17th Conference on Natural Language Processing KONVENS 2021, pages 83-87, Online (2021)
This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembling strategy by majority (hard) voting with multiple different BERT mod
Externí odkaz:
http://arxiv.org/abs/2110.02042