1 citations · 1 across the 9 of their papers we have counts for
11 papers
How smoothing the affinity matrix affects neighborhood preservation in t-SNE
Shirin Mohebi, Guillaume Bied, Jefrey Lijffijt
Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborh…
People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe
Maria-Louisa Wightman, Guillaume Bied, Tijl De Bie
As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing c…
Grokipedia vs Wikipedia: An LLM-Based Audit of Political Neutrality along Ideologies
Filippos Vlahos, Guillaume Bied, Tijl De Bie
Online encyclopedias shape political opinion and, through it, democratic discourse. In late 2025, Grokipedia was released, an encyclopedia written entirely by the LLM Grok. One mot…
STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling
Iman Johary, Guillaume Bied, Alexandru C. Mara +1
Career paths encode decades of skill acquisition, role transitions, and educational investment, and understanding them at scale underpins workforce planning, labor market policy, a…
JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes
Iman Johary, Guillaume Bied, Alexandru C. Mara +1
Large-scale, richly annotated career trajectory data underpins workforce planning, job recommendation, and labour market analysis, yet publicly available datasets are either small,…
Cards Against LLMs: Benchmarking Humor Alignment in Large Language Models
Yousra Fettach, Guillaume Bied, Hannu Toivonen +1
Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimension of Large Language Model (L…
A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
Guillaume Bied, Philippe Caillou, Bruno Crépon +3
Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives…
Building and Measuring Trust between Large Language Models
Maarten Buyl, Yousra Fettach, Guillaume Bied +1
As large language models (LLMs) increasingly interact with each other, most notably in multi-agent setups, we may expect (and hope) that `trust' relationships develop between them,…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
InfoClus: Informative Clustering of High-dimensional Data Embeddings
Fuyin Lai, Edith Heiter, Guillaume Bied +1
Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often d…
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices
Sander Noels, Guillaume Bied, Maarten Buyl +4
Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to…