6 citations · 8 across the 14 of their papers we have counts for
5 papers · 1 filter
Enhancing Fact Retrieval in PLMs through Truthfulness
Paul Youssef, Jörg Schlötterer, Christin Seifert
Pre-trained Language Models (PLMs) encode various facts about the world at their pre-training phase as they are trained to predict the next or missing word in a sentence. There has…
How to Make LLMs Forget: On Reversing In-Context Knowledge Edits
Paul Youssef, Zhixue Zhao, Jörg Schlötterer +1
In-context knowledge editing (IKE) enables efficient modification of large language model (LLM) outputs without parameter changes and at zero-cost. However, it can be misused to ma…
Has this Fact been Edited? Detecting Knowledge Edits in Language Models
Paul Youssef, Zhixue Zhao, Christin Seifert +1
Knowledge editing methods (KEs) can update language models' obsolete or inaccurate knowledge learned from pre-training. However, KEs can be used for malicious applications, e.g., i…
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study
Van Bach Nguyen, Paul Youssef, Christin Seifert +1
As NLP models become more complex, understanding their decisions becomes more crucial. Counterfactuals (CFs), where minimal changes to inputs flip a model's prediction, offer a way…
The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMs
Paul Youssef, Jörg Schlötterer, Christin Seifert
Factual knowledge encoded in Pre-trained Language Models (PLMs) enriches their representations and justifies their use as knowledge bases. Previous work has focused on probing PLMs…