3 citations · 5 across the 13 of their papers we have counts for
13 papers
Persuasion Tokens for Editing Factual Knowledge in LLMs
Paul Youssef, Christin Seifert, Jörg Schlötterer
In-context knowledge editing (IKE) is a promising technique for updating Large Language Models (LLMs) with new information. However, IKE relies on lengthy, fact-specific demonstrat…
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
Yinjie Cheng, Paul Youssef, Christin Seifert +2
Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs). Meanwhile, fine-tuning remains the default operation for adapting L…
Tracing and Reversing Edits in LLMs
Paul Youssef, Zhixue Zhao, Christin Seifert +1
Knowledge editing methods (KEs) are a cost-effective way to update the factual content of large language models (LLMs), but they pose a dual-use risk. While KEs are beneficial for…
Position: Editing Large Language Models Poses Serious Safety Risks
Paul Youssef, Zhixue Zhao, Daniel Braun +2
Large Language Models (LLMs) contain large amounts of facts about the world. These facts can become outdated over time, which has led to the development of knowledge editing method…
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…
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Jan Trienes, Sebastian Joseph, Jörg Schlötterer +5
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework…
Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models
Paul Youssef, Osman Alperen Koraş, Meijie Li +2
Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. This fact has sparked the interest of the community in quantifying the amount of fac…
Case-level Breast Cancer Prediction for Real Hospital Settings
Shreyasi Pathak, Jörg Schlötterer, Jeroen Geerdink +4
Breast cancer prediction models for mammography assume that annotations are available for individual images or regions of interest (ROIs), and that there is a fixed number of image…
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Jan Trienes, Paul Youssef, Jörg Schlötterer +1
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to en…