27 citations · 37 across the 15 of their papers we have counts for
11 papers · 1 filter
MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation
Pia Chouayfati, Alexander M. Fichtl, Miriam Anschütz +2
Clinical diagnosis is fundamentally interactive and incremental, yet the dominant paradigm for evaluating Large Language Models (LLMs) in medicine remains static QA benchmarks or t…
German4All -- A Dataset and Model for Readability-Controlled Paraphrasing in German
Miriam Anschütz, Thanh Mai Pham, Eslam Nasrallah +3
The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce…
Simplifications are Absolutists: How Simplified Language Reduces Word Sense Awareness in LLM-Generated Definitions
Lukas Ellinger, Miriam Anschütz, Georg Groh
Large Language Models (LLMs) can provide accurate word definitions and explanations for any context. However, the scope of the definition changes for different target groups, like…
TUM-MiKaNi at SemEval-2025 Task 3: Towards Multilingual and Knowledge-Aware Non-factual Hallucination Identification
Miriam Anschütz, Ekaterina Gikalo, Niklas Herbster +1
Hallucinations are one of the major problems of LLMs, hindering their trustworthiness and deployment to wider use cases. However, most of the research on hallucinations focuses on…
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?
Miriam Anschütz, Edoardo Mosca, Georg Groh
Text simplification seeks to improve readability while retaining the original content and meaning. Our study investigates whether pre-trained classifiers also maintain such coheren…
This is not correct! Negation-aware Evaluation of Language Generation Systems
Miriam Anschütz, Diego Miguel Lozano, Georg Groh
Large language models underestimate the impact of negations on how much they change the meaning of a sentence. Therefore, learned evaluation metrics based on these models are insen…
Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training
Miriam Anschütz, Joshua Oehms, Thomas Wimmer +2
Automatic text simplification systems help to reduce textual information barriers on the internet. However, for languages other than English, only few parallel data to train these…
AdamR at SemEval-2023 Task 10: Solving the Class Imbalance Problem in Sexism Detection with Ensemble Learning
Adam Rydelek, Daryna Dementieva, Georg Groh
The Explainable Detection of Online Sexism task presents the problem of explainable sexism detection through fine-grained categorisation of sexist cases with three subtasks. Our te…
Adam-Smith at SemEval-2023 Task 4: Discovering Human Values in Arguments with Ensembles of Transformer-based Models
Daniel Schroter, Daryna Dementieva, Georg Groh
This paper presents the best-performing approach alias "Adam Smith" for the SemEval-2023 Task 4: "Identification of Human Values behind Arguments". The goal of the task was to crea…
IFAN: An Explainability-Focused Interaction Framework for Humans and NLP Models
Edoardo Mosca, Daryna Dementieva, Tohid Ebrahim Ajdari +4
Interpretability and human oversight are fundamental pillars of deploying complex NLP models into real-world applications. However, applying explainability and human-in-the-loop me…
From Judgement's Premises Towards Key Points
Oren Sultan, Rayen Dhahri, Yauheni Mardan +2
Key Point Analysis(KPA) is a relatively new task in NLP that combines summarization and classification by extracting argumentative key points (KPs) for a topic from a collection of…