collaborators

7 papers

cs.CL2025

Multilingual Datasets for Custom Input Extraction and Explanation Requests Parsing in Conversational XAI Systems

Qianli Wang, Tatiana Anikina, Nils Feldhus +6

Conversational explainable artificial intelligence (ConvXAI) systems based on large language models (LLMs) have garnered considerable attention for their ability to enhance user co…

cs.CL2025

Table Understanding and (Multimodal) LLMs: A Cross-Domain Case Study on Scientific vs. Non-Scientific Data

Ekaterina Borisova, Fabio Barth, Nils Feldhus +5

Tables are among the most widely used tools for representing structured data in research, business, medicine, and education. Although LLMs demonstrate strong performance in downstr…

cs.CL2025

Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals

Qianli Wang, Van Bach Nguyen, Nils Feldhus +4

Counterfactual examples are widely employed to enhance the performance and robustness of large language models (LLMs) through counterfactual data augmentation (CDA). However, the s…

cs.CL2025

Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models

Sebastian Möller, Pia Knoeferle, Britta Schulte +1

Machine learning techniques have conquered many different tasks in speech and natural language processing, such as speech recognition, information extraction, text and speech gener…

cs.CL2025

Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution

Tatiana Anikina, Arne Binder, David Harbecke +5

In this work, we reimagine classical probing to evaluate knowledge transfer from simple source to more complex target tasks. Instead of probing frozen representations from a comple…

cs.CL2025

FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation

Qianli Wang, Nils Feldhus, Simon Ostermann +3

Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand mode…