R Chadwick, S Blundell, E Prendergast - 2024 - files.osf.io
The increasing deployment of artificial intelligence systems in various applications has intensified concerns regarding their compliance with stringent privacy regulations …
Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with …
Learning effective representations from raw data is crucial for the success of deep learning methods. However, in the tabular domain, practitioners often prefer augmenting raw column …
R Shan, Q Ming, G Hong, H Wu - 2024 - files.osf.io
To evaluate the hallucination tendencies of state-ofthe-art language models is crucial for improving their reliability and applicability across various domains. This article presents a …
J Hawthorne, F Radcliffe, L Whitaker - 2024 - files.osf.io
Ensuring the semantic validity of text generated through artificial intelligence remains a significant challenge, particularly as applications in various domains demand higher …
Z Wang, Y Li, Y Zhang - Authorea Preprints, 2024 - techrxiv.org
Abstract Knowledge graphs have become essential tools for structuring and representing complex information across various domains, enabling advanced applications such as …
R Dzierzanowski, T Watson, C Stevenson - 2024 - researchsquare.com
Evaluating the effectiveness of ChatGPT in scrutinizing and debunking misinformation embedded within social media memes reveals significant potential for AI-driven fact …
C Hautzenberger, S Muellen - Authorea Preprints, 2024 - techrxiv.org
Generative artificial intelligence systems, such as OpenAI ChatGPT and Anthropic Claude, have shown remarkable capabilities in generating human-like text responses, raising …
Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge …