9 citations · 9 across the 8 of their papers we have counts for
8 papers
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
Mahdi Dhaini, Adam Dejl, Juraj Vladika +3
Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it re…
Facts Fade Fast: Evaluating Memorization of Outdated Medical Knowledge in Large Language Models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…
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…
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…
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…
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…
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…
Detecting ChatGPT: A Survey of the State of Detecting ChatGPT-Generated Text
Mahdi Dhaini, Wessel Poelman, Ege Erdogan
While recent advancements in the capabilities and widespread accessibility of generative language models, such as ChatGPT (OpenAI, 2022), have brought about various benefits by gen…