@inproceedings{jung-jung-2025-courtroom,
    title = "Courtroom-{LLM}: A Legal-Inspired Multi-{LLM} Framework for Resolving Ambiguous Text Classifications",
    author = "Jung, Sangkeun  and
      Jung, Jeesu",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.493/",
    pages = "7367--7385",
    abstract = "In this research, we introduce the Courtroom-LLM framework, a novel multi-LLM structure inspired by legal courtroom processes, aiming to enhance decision-making in ambiguous text classification scenarios. Our approach simulates a courtroom setting within LLMs, assigning roles similar to those of prosecutors, defense attorneys, and judges, to facilitate comprehensive analysis of complex textual cases. We demonstrate that this structured multi-LLM setup can significantly improve decision-making accuracy, particularly in ambiguous situations, by harnessing the synergistic effects of diverse LLM arguments. Our evaluations across various text classification tasks show that the Courtroom-LLM framework outperforms both traditional single-LLM classifiers and simpler multi-LLM setups. These results highlight the advantages of our legal-inspired model in improving decision-making for text classification."
}