4 citations · 22 across the 35 of their papers we have counts for
12 papers · 1 filter
XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders
Khawla Elhadri, Jörg Schlötterer, Christin Seifert
In data-driven applications relying on tabular data, where interpretability is key, machine learning models such as decision trees and linear regression are applied. Although neura…
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…
Towards Interpretable Deep Neural Networks for Tabular Data
Khawla Elhadri, Jörg Schlötterer, Christin Seifert
Tabular data is the foundation of many applications in fields such as finance and healthcare. Although DNNs tailored for tabular data achieve competitive predictive performance, th…
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…
Invariant Learning with Annotation-free Environments
Phuong Quynh Le, Christin Seifert, Jörg Schlötterer
Invariant learning is a promising approach to improve domain generalization compared to Empirical Risk Minimization (ERM). However, most invariant learning methods rely on the assu…
An XAI-based Analysis of Shortcut Learning in Neural Networks
Phuong Quynh Le, Jörg Schlötterer, Christin Seifert
Machine learning models tend to learn spurious features - features that strongly correlate with target labels but are not causal. Existing approaches to mitigate models' dependence…
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…
This looks like what? Challenges and Future Research Directions for Part-Prototype Models
Khawla Elhadri, Tomasz Michalski, Adam Wróbel +3
The growing interest in eXplainable Artificial Intelligence (XAI) has stimulated research on models with built-in interpretability, among which part-prototype models are particular…
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…
Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers
Lukas Kuhn, Sari Sadiya, Jorg Schlotterer +3
Shortcut learning, i.e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning…