Search Results for author:
Found 5 papers, 3 papers with code
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations
Pre-training of neural networks has recently revolutionized the field of Natural Language Processing (NLP) and has before demonstrated its effectiveness in computer vision.
Learning to Prompt in the Classroom to Understand AI Limits: A pilot study
Encouraging preliminary results emerged, including high appreciation of the activity, improved interaction quality with the LLM, reduced negative AI sentiments, and a better grasp of limitations, specifically unreliability, limited understanding of commands leading to unsatisfactory responses, and limited presentation flexibility.
Developing Effective Educational Chatbots with ChatGPT prompts: Insights from Preliminary Tests in a Case Study on Social Media Literacy (with appendix)
Educational chatbots come with a promise of interactive and personalized learning experiences, yet their development has been limited by the restricted free interaction capabilities of available platforms and the difficulty of encoding knowledge in a suitable format.
Exploring Fake News Detection with Heterogeneous Social Media Context Graphs
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.
A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts
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.
