collaborators

6 papers

cs.CL2025

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study

Mahdi Dhaini, Juraj Vladika, Ege Erdogan +2

In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and…

cs.CL2025

Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models

Juraj Vladika, Mahdi Dhaini, Florian Matthes

The growing capabilities of Large Language Models (LLMs) show significant potential to enhance healthcare by assisting medical researchers and physicians. However, their reliance o…

cs.CL2025

When Explainability Meets Privacy: An Investigation at the Intersection of Post-hoc Explainability and Differential Privacy in the Context of Natural Language Processing

Mahdi Dhaini, Stephen Meisenbacher, Ege Erdogan +2

In the study of trustworthy Natural Language Processing (NLP), a number of important research fields have emerged, including that of explainability and privacy. While research inte…

cs.CL2025

Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges

Mahdi Dhaini, Tobias Müller, Roksoliana Rabets +1

The field of explainable natural language processing (NLP) has grown rapidly in recent years. The growing opacity of complex models calls for transparency and explanations of their…

cs.CL2025

EvalxNLP: A Framework for Benchmarking Post-Hoc Explainability Methods on NLP Models

Mahdi Dhaini, Kafaite Zahra Hussain, Efstratios Zaradoukas +1

As Natural Language Processing (NLP) models continue to evolve and become integral to high-stakes applications, ensuring their interpretability remains a critical challenge. Given…

cs.CL2025

Gender Bias in Explainability: Investigating Performance Disparity in Post-hoc Methods

Mahdi Dhaini, Ege Erdogan, Nils Feldhus +1

While research on applications and evaluations of explanation methods continues to expand, fairness of the explanation methods concerning disparities in their performance across su…