Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Groh Georg"'
Autor:
Pan Yan, Bernhard Lukas, Fan Cheng, Beckendorf Lukas, Wilhelm Dirk, Feußner Hubertus, Groh Georg
Publikováno v:
Current Directions in Biomedical Engineering, Vol 10, Iss 2, Pp 54-57 (2024)
To help with the critical nurse staffing shortages in hospitals worldwide, robotic assistants are designed to handle frequently required tasks in the digital operating room (DOR), such as the guidance of the laparoscopic camera. To enable fluent coll
Externí odkaz:
https://doaj.org/article/e18d4b009617478f9efe55afb93e9542
In the era dominated by information overload and its facilitation with Large Language Models (LLMs), the prevalence of misinformation poses a significant threat to public discourse and societal well-being. A critical concern at present involves the i
Externí odkaz:
http://arxiv.org/abs/2408.10724
The task of toxicity detection is still a relevant task, especially in the context of safe and fair LMs development. Nevertheless, labeled binary toxicity classification corpora are not available for all languages, which is understandable given the r
Externí odkaz:
http://arxiv.org/abs/2404.17841
Text simplification seeks to improve readability while retaining the original content and meaning. Our study investigates whether pre-trained classifiers also maintain such coherence by comparing their predictions on both original and simplified inpu
Externí odkaz:
http://arxiv.org/abs/2404.06838
Despite the extensive amount of labeled datasets in the NLP text classification field, the persistent imbalance in data availability across various languages remains evident. Ukrainian, in particular, stands as a language that still can benefit from
Externí odkaz:
http://arxiv.org/abs/2404.02043
Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insensitive to negations. In this paper, we propose NegBLEURT, a negation-a
Externí odkaz:
http://arxiv.org/abs/2307.13989
Automatic text simplification systems help to reduce textual information barriers on the internet. However, for languages other than English, only few parallel data to train these systems exists. We propose a two-step approach to overcome this data s
Externí odkaz:
http://arxiv.org/abs/2305.12908
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our team experimented with different ways to combat class imbalance througho
Externí odkaz:
http://arxiv.org/abs/2305.08636
This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments". The goal of the task was to create systems that automatically identify the values within textual argum
Externí odkaz:
http://arxiv.org/abs/2305.08625
Autor:
Mosca, Edoardo, Dementieva, Daryna, Ajdari, Tohid Ebrahim, Kummeth, Maximilian, Gringauz, Kirill, Zhou, Yutong, Groh, Georg
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop methods requires technical proficiency. Despite existing toolkits for mo
Externí odkaz:
http://arxiv.org/abs/2303.03124