170 citations · 516 across the 41 of their papers we have counts for
6 papers · 1 filter
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…