Zobrazeno 1 - 10
of 3 120
pro vyhledávání: '"Borchert P"'
Large Language Models (LLMs) excel at providing information acquired during pretraining on large-scale corpora and following instructions through user prompts. This study investigates whether the quality of LLM responses varies depending on the demog
Externí odkaz:
http://arxiv.org/abs/2406.17385
LLMs have become a go-to solution not just for text generation, but also for natural language understanding (NLU) tasks. Acquiring extensive knowledge through language modeling on web-scale corpora, they excel on English NLU, yet struggle to extend t
Externí odkaz:
http://arxiv.org/abs/2406.12739
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the domain, entities, and relations
Externí odkaz:
http://arxiv.org/abs/2403.16543
Autor:
Avramidis, Kleanthis, Chang, Melinda Y., Sharma, Rahul, Borchert, Mark S., Narayanan, Shrikanth
A wide range of neurological and cognitive disorders exhibit distinct behavioral markers aside from their clinical manifestations. Cortical Visual Impairment (CVI) is a prime example of such conditions, resulting from damage to visual pathways in the
Externí odkaz:
http://arxiv.org/abs/2402.09655
This study explores the intersection of information technology-based self-monitoring (ITSM) and emotional responses in chronic care. It critiques the lack of theoretical depth in current ITSM research and proposes a dynamic emotion process theory to
Externí odkaz:
http://arxiv.org/abs/2311.05449
Autor:
Borchert, Philipp, De Weerdt, Jochen, Coussement, Kristof, De Caigny, Arno, Moens, Marie-Francine
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages.
Externí odkaz:
http://arxiv.org/abs/2310.12024
Objective: To improve performance of medical entity normalization across many languages, especially when fewer language resources are available compared to English. Materials and Methods: We introduce xMEN, a modular system for cross-lingual medical
Externí odkaz:
http://arxiv.org/abs/2310.11275
This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we dem
Externí odkaz:
http://arxiv.org/abs/2310.10310
Question answering over hybrid contexts is a complex task, which requires the combination of information extracted from unstructured texts and structured tables in various ways. Recently, In-Context Learning demonstrated significant performance advan
Externí odkaz:
http://arxiv.org/abs/2310.06675
Autor:
Parisa Faraji, Elham Parandavar, Hartmut Kuhn, Mehran Habibi-Rezaei, Astrid Borchert, Elham Zahedi, Shahin Ahmadian
Publikováno v:
Molecular Medicine, Vol 30, Iss 1, Pp 1-19 (2024)
Abstract Background Alzheimer’s disease (AD) is the most common human neurodegenerative disorder worldwide. Owing to its chronic nature, our limited understanding of its pathophysiological mechanisms, and because of the lack of effective anti-AD dr
Externí odkaz:
https://doaj.org/article/0d1011b3252b422a8f183d9c69122c08
