62 citations · 105 across the 12 of their papers we have counts for
12 papers
Evaluating and Mitigating Anti-LGBTQ Biases in German and Multilingual Language Models
Melina Morch, Daniel Braun
While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Existing benchmarks often do not ca…
Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
Olufunke O. Sarumi, Charles Welch, Daniel Braun
Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework…
Acquiescence Bias in Large Language Models
Daniel Braun
Acquiescence bias, i.e. the tendency of humans to agree with statements in surveys, independent of their actual beliefs, is well researched and documented. Since Large Language Mod…
A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
Oleg Silcenco, Marcos R. Machad, Wallace C. Ugulino +1
Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study intro…
The Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum
Olufunke O. Sarumi, Charles Welch, Daniel Braun +1
In this work, we explore the capability of Large Language Models (LLMs) to annotate hate speech and abusiveness while considering predefined annotator personas within the strong-to…
Position: Editing Large Language Models Poses Serious Safety Risks
Paul Youssef, Zhixue Zhao, Daniel Braun +2
Large Language Models (LLMs) contain large amounts of facts about the world. These facts can become outdated over time, which has led to the development of knowledge editing method…
AGB-DE: A Corpus for the Automated Legal Assessment of Clauses in German Consumer Contracts
Daniel Braun, Florian Matthes
Legal tasks and datasets are often used as benchmarks for the capabilities of language models. However, openly available annotated datasets are rare. In this paper, we introduce AG…
Efficient Black-Box Adversarial Attacks on Neural Text Detectors
Vitalii Fishchuk, Daniel Braun
Neural text detectors are models trained to detect whether a given text was generated by a language model or written by a human. In this paper, we investigate three simple and reso…
Challenges in Domain-Specific Abstractive Summarization and How to Overcome them
Anum Afzal, Juraj Vladika, Daniel Braun +1
Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as d…
Investigating Conversational Search Behavior For Domain Exploration
Phillip Schneider, Anum Afzal, Juraj Vladika +2
Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search age…
Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches
Tim Schopf, Daniel Braun, Florian Matthes
Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches…
Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics
Tim Schopf, Daniel Braun, Florian Matthes
In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised appr…