3 citations · 8 across the 24 of their papers we have counts for
9 papers · 1 filter
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…
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support
Jan Trienes, Anastasiia Derzhanskaia, Roland Schwarzkopf +3
We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and…
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…
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
Van Bach Nguyen, Christin Seifert, Jörg Schlötterer
The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. C…
Behavioral Analysis of Information Salience in Large Language Models
Jan Trienes, Jörg Schlötterer, Junyi Jessy Li +1
Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs hav…
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 These facts can become outdated over time, which has led to the development of knowledge editing method…