@unpublished{BornheimGriegerBlanecketal.2023,
  author    = {Bornheim, Tobias and Grieger, Niklas and Blaneck, Patrick Gustav and Bialonski, Stephan},
  title     = {Preprint: Speaker attribution in German parliamentary debates with QLoRA-adapted large language models},
  series = {Journal for Language Technology and Computational Linguistics},
  journal   = {Journal for Language Technology and Computational Linguistics},
  doi       = {10.48550/arXiv.2309.09902},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.},
  language  = {en}
}
@inproceedings{MaurerMiskiwAcostaetal.2023,
  author    = {Maurer, Florian and Miskiw, Kim K. and Acosta, Rebeca Ramirez and Harder, Nick and Sander, Volker and Lehnhoff, Sebastian},
  title     = {Market abstraction of energy markets and policies - application in an agent-based modeling toolbox},
  series = {EI.A 2023: Energy Informatics},
  booktitle = {EI.A 2023: Energy Informatics},
  editor    = {Jorgensen, Bo Norregaard and Pereira da Silva, Luiz Carlos and Ma, Zheng},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-031-48651-7 (Print)},
  doi       = {10.1007/978-3-031-48652-4_10},
  pages     = {139 -- 157},
  year      = {2023},
  abstract  = {In light of emerging challenges in energy systems, markets are prone to changing dynamics and market design. Simulation models are commonly used to understand the changing dynamics of future electricity markets. However, existing market models were often created with specific use cases in mind, which limits their flexibility and usability. This can impose challenges for using a single model to compare different market designs. This paper introduces a new method of defining market designs for energy market simulations. The proposed concept makes it easy to incorporate different market designs into electricity market models by using relevant parameters derived from analyzing existing simulation tools, morphological categorization and ontologies. These parameters are then used to derive a market abstraction and integrate it into an agent-based simulation framework, allowing for a unified analysis of diverse market designs. Furthermore, we showcase the usability of integrating new types of long-term contracts and over-the-counter trading. To validate this approach, two case studies are demonstrated: a pay-as-clear market and a pay-as-bid long-term market. These examples demonstrate the capabilities of the proposed framework.},
  language  = {en}
}