1.2k citations · 1.2k across the 11 of their papers we have counts for
12 papers
Explainability-Based Token Replacement on LLM-Generated Text
Hadi Mohammadi, Anastasia Giachanou, Daniel L. Oberski +1
Generative models, especially large language models (LLMs), have shown remarkable progress in producing text that appears human-like. However, they often exhibit patterns that make…
Explainability in Practice: A Survey of Explainable NLP Across Various Domains
Hadi Mohammadi, Robert A. Bagheri, Anastasia Giachanou +1
Natural Language Processing (NLP) is now embedded in critical sectors including healthcare, finance, and customer relationship management, where models such as GPT-4o, Gemini, and…
PATCH! {P}sychometrics-{A}ssis{T}ed Ben{CH}marking of Large Language Models against Human Populations: A Case Study of Proficiency in 8th Grade Mathematics
Qixiang Fang, Daniel L. Oberski, Dong Nguyen
Many existing benchmarks of large (multimodal) language models (LLMs) focus on measuring LLMs' academic proficiency, often with also an interest in comparing model performance with…
General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study
Qixiang Fang, Zhihan Zhou, Francesco Barbieri +6
Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly y…
On Text-based Personality Computing: Challenges and Future Directions
Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri +4
Text-based personality computing (TPC) has gained many research interests in NLP. In this paper, we describe 15 challenges that we consider deserving the attention of the research…
Evaluating the Construct Validity of Text Embeddings with Application to Survey Questions
Qixiang Fang, Dong Nguyen, Daniel L Oberski
Text embedding models from Natural Language Processing can map text data (e.g. words, sentences, documents) to supposedly meaningful numerical representations (a.k.a. text embeddin…
Privacy preserving local analysis of digital trace data: A proof-of-concept
Laura Boeschoten, Adriënne Mendrik, Emiel van der Veen +4
We present PORT, a software platform for local data extraction and analysis of digital trace data. While digital trace data collected by private and public parties hold a huge pote…
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data
Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis +3
Electronic health records (EHRs) contain structured and unstructured data of significant clinical and research value. Various machine learning approaches have been developed to emp…
Open Source Software for Efficient and Transparent Reviews
Rens van de Schoot, Jonathan de Bruin, Raoul Schram +14
To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, we designed a tool (ASReview) to accelerate the step of screening tit…
The effect of measurement error on clustering algorithms
Paulina Pankowska, Daniel L. Oberski
Clustering consists of a popular set of techniques used to separate data into interesting groups for further analysis. Many data sources on which clustering is performed are well-k…
Fair inference on error-prone outcomes
Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri +1
Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting gr…
A test for monitoring under- and overtreatment in Dutch hospitals
Oliver Urs Lenz, Daniel L Oberski
Over- and undertreatment harm patients and society and confound other healthcare quality measures. Despite a growing body of research covering specific conditions, we lack tools to…