
Pepa Atanasova
explainability
interpretability
instance attribution
fact checking
natural language inference
faithfulness
xai
neuron
interactions
video question answering
parameter-efficient training
parametric knowledge
neuron attribution
data-efficient training
nlp in resource-constrained settings
5
presentations
2
number of views
SHORT BIO
I am a final year Ph.D. student at the University of Copenhagen, CopeNLU group, supervised by Isabelle Augenstein and co-supervised by Christina Lioma, and Jakob Grue Simonsen. My current research focus is explainability for machine learning models, encompassing natural language explanations, post-hoc explainability methods, and adversarial attacks as well as the principled evaluation of existing explainability techniques. My work is currently centered on the application area of knowledge-intensive and complex reasoning natural language tasks, such as fact checking and question answering.
Presentations

From Internal Conflict to Contextual Adaptation of Language Models
Sara Vera Marjanovic and 5 other authors

Revealing the Parametric Knowledge of Language Models: A Unified Framework for Attribution Methods
Haeun Yu and 2 other authors

Explaining Interactions Between Text Spans
Sagnik Choudhury and 2 other authors

Fact Checking with Insufficient Evidence
Pepa Atanasova

Diagnostics-Guided Explanation Generation
Pepa Atanasova and 3 other authors