10 citations · 20 across the 12 of their papers we have counts for
8 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…
Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering
Lukas Ellinger, Alexander Fichtl, Miriam Anschütz +1
Natural language conveys information at varying levels of granularity, from fine-grained references to broad descriptions. While granularity is fundamental to human communication,…
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…