24 citations · 64 across the 10 of their papers we have counts for
13 papers
EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs
Ali Satvaty, Suzan Verberne, Fatih Turkmen
Membership inference attacks (MIA) aim to infer whether a particular data point is part of the training dataset of a model. In this paper, we propose a new task in the context of L…
Dataset Creation for Visual Entailment using Generative AI
Rob Reijtenbach, Suzan Verberne, Gijs Wijnholds
In this paper we present and validate a new synthetic dataset for training visual entailment models. Existing datasets for visual entailment are small and sparse compared to datase…
Undesirable Memorization in Large Language Models: A Survey
Ali Satvaty, Suzan Verberne, Fatih Turkmen
While recent research increasingly showcases the remarkable capabilities of Large Language Models (LLMs), it is equally crucial to examine their associated risks. Among these, priv…
Efficient course recommendations with T5-based ranking and summarization
Thijmen Bijl, Niels van Weeren, Suzan Verberne
In this paper, we implement and evaluate a two-stage retrieval pipeline for a course recommender system that ranks courses for skill-occupation pairs. The in-production recommender…
Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus
Fons Hartendorp, Tom Seinen, Erik van Mulligen +1
Biomedical entity linking, a main component in automatic information extraction from health-related texts, plays a pivotal role in connecting textual entities (such as diseases, dr…
ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science
Bram M. A. van Dijk, Max J. van Duijn, Suzan Verberne +1
In this resource paper we release ChiSCor, a new corpus containing 619 fantasy stories, told freely by 442 Dutch children aged 4-12. ChiSCor was compiled for studying how children…
Political corpus creation through automatic speech recognition on EU debates
Hugo de Vos, Suzan Verberne
In this paper, we present a transcribed corpus of the LIBE committee of the EU parliament, totalling 3.6 Million running words. The meetings of parliamentary committees of the EU a…
Small data problems in political research: a critical replication study
Hugo de Vos, Suzan Verberne
In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos & Whitford (A&W) propose a text classification method for tweets related to organi…
Helping results assessment by adding explainable elements to the deep relevance matching model
Ioannis Chios, Suzan Verberne
In this paper we address the explainability of web search engines. We propose two explainable elements on the search engine result page: a visualization of query term weights and a…
Two tales of science technology linkage: Patent in-text versus front-page references
Jian Wang, Suzan Verberne
There is recurrent debate about how useful science is for technological development, but we know little about what kinds of science are more useful for technology. This paper fills…
Improving reference mining in patents with BERT
Ken Voskuil, Suzan Verberne
In this paper we address the challenge of extracting scientific references from patents. We approach the problem as a sequence labelling task and investigate the merits of BERT mod…
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering
Jeroen Offerijns, Suzan Verberne, Tessa Verhoef
For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question genera…
The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviews
Benjamin van der Burgh, Suzan Verberne
We evaluated the effectiveness of using language models, that were pre-trained in one domain, as the basis for a classification model in another domain: Dutch book reviews. Pre-tra…