13 citations · 14 across the 6 of their papers we have counts for
6 papers
Seeking Information with RAG-Assistants: Does Model Size Matter in Human-AI Collaborations?
Lennard C. Froma, Tom Kouwenhoven, Maaike H. T. de Boer +2
Much research on LLMs has focused on increasing benchmark performance. However, the evaluation of such models in real-world collaborative human-AI workflows has stayed behind. This…
"I Don't Know" -- Towards Appropriate Trust with Certainty-Aware Retrieval Augmented Generation
Daan Di Scala, Maaike de Boer, Pınar Yolum
Achieving the right amount of trust in AI systems is important, but challenging. The problem is exacerbated with the rise of Large Language Models (LLMs) as they provide human-leve…
Flexible image analysis for law enforcement agencies with deep neural networks to determine: where, who and what
Henri Bouma, Bart Joosten, Maarten C Kruithof +4
Due to the increasing need for effective security measures and the integration of cameras in commercial products, a hugeamount of visual data is created today. Law enforcement agen…
Modular Design Patterns for Hybrid Actors
André Meyer-Vitali, Wico Mulder, Maaike H. T. de Boer
Recently, a boxology (graphical language) with design patterns for hybrid AI was proposed, combining symbolic and sub-symbolic learning and reasoning. In this paper, we extend this…
Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases
Michael van Bekkum, Maaike de Boer, Frank van Harmelen +2
The unification of statistical (data-driven) and symbolic (knowledge-driven) methods is widely recognised as one of the key challenges of modern AI. Recent years have seen large nu…
Gender prediction using limited Twitter Data
Maaike Burghoorn, Maaike H. T. de Boer, Stephan Raaijmakers
Transformer models have shown impressive performance on a variety of NLP tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP classification tasks, reducing…