170 citations · 516 across the 41 of their papers we have counts for
45 papers
Cultural (In)Competence: Investigating Culture-Specific Stereotype Detection Capabilities of LLMs
Maximilian Spliethöver, Jannes Lötfering, Dominik Bachmann +4
Stereotypes are subjective constructs that are mediated by the cultural context in which they are viewed. Successfully modeling them thus also requires cultural competence. When us…
Generating Constructive Feedback on Stories via Reinforcement Learning
Maja Stahl, Timon Ziegenbein, Henning Wachsmuth
Constructive feedback is crucial for creative writers to refine their storytelling abilities. Since receiving feedback from human experts is often costly and time-intensive, large…
Attribute-Based Activation Steering of LLMs for Group-Specific Explanation Generation
Leandra Fichtel, Janek Prange, Henning Wachsmuth
To effectively enable people to understand new topics, explanations should be tailored to their backgrounds and abilities. Prompting alone has been shown to be insufficient for cre…
ArgBench: Benchmarking LLMs on Computational Argumentation Tasks
Yamen Ajjour, Carlotta Quensel, Nedim Lipka +1
Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection, debating collaboratively f…
Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning
Timon Ziegenbein, Maja Stahl, Henning Wachsmuth
Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a discussion. Comparing human t…
Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
Lukas Fehring, Marcel Wever, Maximilian Spliethöver +3
Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate expert knowledge during initializatio…
Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
Elena Musi, Nadin Kokciyan, Khalid Al-Khatib +10
In this position paper, we advocate for the development of conversational technology that is inherently designed to support and facilitate argumentative processes. We argue that, a…
ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation
Maja Stahl, Timon Ziegenbein, Joonsuk Park +1
Training large language models (LLMs) to follow instructions has significantly enhanced their ability to tackle unseen tasks. However, despite their strong generalization capabilit…
Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues
Leandra Fichtel, Maximilian Spliethöver, Eyke Hüllermeier +9
The ability to generate explanations that are understood by explainees is the quintessence of explainable artificial intelligence. Since understanding depends on the explainee's ba…
Towards a Perspectivist Turn in Argument Quality Assessment
Julia Romberg, Maximilian Maurer, Henning Wachsmuth +1
The assessment of argument quality depends on well-established logical, rhetorical, and dialectical properties that are unavoidably subjective: multiple valid assessments may exist…
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
Maximilian Spliethöver, Tim Knebler, Fabian Fumagalli +4
Recent advances on instruction fine-tuning have led to the development of various prompting techniques for large language models, such as explicit reasoning steps. However, the suc…
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
Maximilian Spliethöver, Sai Nikhil Menon, Henning Wachsmuth
Dialects introduce syntactic and lexical variations in language that occur in regional or social groups. Most NLP methods are not sensitive to such variations. This may lead to unf…
LLM-based Rewriting of Inappropriate Argumentation using Reinforcement Learning from Machine Feedback
Timon Ziegenbein, Gabriella Skitalinskaya, Alireza Bayat Makou +1
Ensuring that online discussions are civil and productive is a major challenge for social media platforms. Such platforms usually rely both on users and on automated detection tool…
Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation
Maja Stahl, Leon Biermann, Andreas Nehring +1
Individual feedback can help students improve their essay writing skills. However, the manual effort required to provide such feedback limits individualization in practice. Automat…
A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality
Maja Stahl, Nadine Michel, Sebastian Kilsbach +3
Learning argumentative writing is challenging. Besides writing fundamentals such as syntax and grammar, learners must select and arrange argument components meaningfully to create…
Argument Quality Assessment in the Age of Instruction-Following Large Language Models
Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio +5
The computational treatment of arguments on controversial issues has been subject to extensive NLP research, due to its envisioned impact on opinion formation, decision making, wri…
Modeling the Quality of Dialogical Explanations
Milad Alshomary, Felix Lange, Meisam Booshehri +3
Explanations are pervasive in our lives. Mostly, they occur in dialogical form where an {\em explainer} discusses a concept or phenomenon of interest with an {\em explainee}. Leavi…
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
Alexander Tornede, Difan Deng, Theresa Eimer +8
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years. In NLP, especially Large Language…
To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support
Gabriella Skitalinskaya, Henning Wachsmuth
Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should…
Modeling Appropriate Language in Argumentation
Timon Ziegenbein, Shahbaz Syed, Felix Lange +2
Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existi…
Perspectives on Large Language Models for Relevance Judgment
Guglielmo Faggioli, Laura Dietz, Charles Clarke +8
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in ev…
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary +11
We present the Touché23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we…
Conclusion-based Counter-Argument Generation
Milad Alshomary, Henning Wachsmuth
In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural…
Claim Optimization in Computational Argumentation
Gabriella Skitalinskaya, Maximilian Spliethöver, Henning Wachsmuth
An optimal delivery of arguments is key to persuasion in any debate, both for humans and for AI systems. This requires the use of clear and fluent claims relevant to the given deba…