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cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2024

CEval: A Benchmark for Evaluating Counterfactual Text Generation

Van Bach Nguyen, Jörg Schlötterer, Christin Seifert

Counterfactual text generation aims to minimally change a text, such that it is classified differently. Judging advancements in method development for counterfactual text generatio…

cs.CL2024

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer +17

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate…