14 citations · 23 across the 24 of their papers we have counts for
24 papers
MUCnoHARM@GermEval Shared Task 2026: Retrieval-based In-Context Learning for Defamatory Offences, and Where It Falls Short
Kristin Gnadt, Maximilian Meidinger, Matthias Aßenmacher
With hate speech being ubiquitous online, automatic detection is crucial, in particular when it comes to criminally relevant social media posts. We study a variety of retrieval-bas…
Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes
Charlotte Leininger, Helena Veit, Matthias Aßenmacher +1
Large language models (LLMs) are increasingly integrated into AI-assisted hiring pipelines, including automated resume generation and screening. Under the EU AI Act, the hiring dom…
GUARD: Glocal Uncertainty-Aware Robust Decoding for Effective and Efficient Open-Ended Text Generation
Yuanhao Ding, Esteban Garces Arias, Meimingwei Li +6
Open-ended text generation faces a critical challenge: balancing coherence with diversity in LLM outputs. While contrastive search-based decoding strategies have emerged to address…
Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher +3
Algorithmic systems such as search engines and information retrieval platforms significantly influence academic visibility and the dissemination of knowledge. Despite assumptions o…
Fair Play in the Newsroom: Actor-Based Filtering Gender Discrimination in Text Corpora
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher +2
Language corpora are the foundation of most natural language processing research, yet they often reproduce structural inequalities. One such inequality is gender discrimination in…
Statistical Multicriteria Evaluation of LLM-Generated Text
Esteban Garces Arias, Hannah Blocher, Julian Rodemann +2
Assessing the quality of LLM-generated text remains a fundamental challenge in natural language processing. Current evaluation approaches often rely on isolated metrics or simplist…
Unveiling Factors for Enhanced POS Tagging: A Study of Low-Resource Medieval Romance Languages
Matthias Schöffel, Esteban Garces Arias, Marinus Wiedner +4
Part-of-speech (POS) tagging remains a foundational component in natural language processing pipelines, particularly critical for historical text analysis at the intersection of co…
taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher +2
Open-access corpora are essential for advancing natural language processing (NLP) and computational social science (CSS). However, large-scale resources for German remain limited,…
No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success
Lukas Rauch, Moritz Wirth, Denis Huseljic +3
The advent of large language models (LLMs) capable of producing general-purpose representations lets us revisit the practicality of deep active learning (AL): By leveraging frozen…
Explainable Coarse-to-Fine Ancient Manuscript Duplicates Discovery
Chongsheng Zhang, Shuwen Wu, Yingqi Chen +5
Ancient manuscripts are the primary source of ancient linguistic corpora. However, many ancient manuscripts exhibit duplications due to unintentional repeated publication or delibe…
Modern Models, Medieval Texts: A POS Tagging Study of Old Occitan
Matthias Schöffel, Marinus Wiedner, Esteban Garces Arias +3
Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their effectiveness in handling historical languages remains largely unex…
Towards Better Open-Ended Text Generation: A Multicriteria Evaluation Framework
Esteban Garces Arias, Hannah Blocher, Julian Rodemann +3
Open-ended text generation has become a prominent task in natural language processing due to the rise of powerful (large) language models. However, evaluating the quality of these…
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation
Esteban Garces Arias, Meimingwei Li, Christian Heumann +1
Decoding strategies for generative large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Guided by specific hyperparameters, these st…
Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation
Luis Mayer, Christian Heumann, Matthias Aßenmacher
In recent years, large language models (LLMs) have emerged as powerful tools with potential applications in various fields, including software engineering. Within the scope of this…
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
Esteban Garces Arias, Julian Rodemann, Meimingwei Li +2
Decoding from the output distributions of large language models to produce high-quality text is a complex challenge in language modeling. Various approaches, such as beam search, s…
How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responses
Stefanie Urchs, Veronika Thurner, Matthias Aßenmacher +2
With the introduction of ChatGPT, OpenAI made large language models (LLM) accessible to users with limited IT expertise. However, users with no background in natural language proce…
A tailored Handwritten-Text-Recognition System for Medieval Latin
Philipp Koch, Gilary Vera Nuñez, Esteban Garces Arias +4
The Bavarian Academy of Sciences and Humanities aims to digitize its Medieval Latin Dictionary. This dictionary entails record cards referring to lemmas in medieval Latin, a low-re…
Classifying multilingual party manifestos: Domain transfer across country, time, and genre
Matthias Aßenmacher, Nadja Sauter, Christian Heumann
Annotating costs of large corpora are still one of the main bottlenecks in empirical social science research. On the one hand, making use of the capabilities of domain transfer all…
How Different Is Stereotypical Bias Across Languages?
Ibrahim Tolga Öztürk, Rostislav Nedelchev, Christian Heumann +4
Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different…
ActiveGLAE: A Benchmark for Deep Active Learning with Transformers
Lukas Rauch, Matthias Aßenmacher, Denis Huseljic +3
Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive…
Benchmarking down-scaled (not so large) pre-trained language models
M. Aßenmacher, P. Schulze, C. Heumann
Large Transformer-based language models are pre-trained on corpora of varying sizes, for a different number of steps and with different batch sizes. At the same time, more fundamen…
A Bayesian approach to modeling topic-metadata relationships
P. Schulze, S. Wiegrebe, P. W. Thurner +2
The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically releva…
Re-Evaluating GermEval17 Using German Pre-Trained Language Models
M. Aßenmacher, A. Corvonato, C. Heumann
The lack of a commonly used benchmark data set (collection) such as (Super-)GLUE (Wang et al., 2018, 2019) for the evaluation of non-English pre-trained language models is a severe…
Pre-trained language models as knowledge bases for Automotive Complaint Analysis
V. D. Viellieber, M. Aßenmacher
Recently it has been shown that large pre-trained language models like BERT (Devlin et al., 2018) are able to store commonsense factual knowledge captured in its pre-training corpu…