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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

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