@inproceedings{cabessa-etal-2025-argument,
    title = "Argument Mining with Fine-Tuned Large Language Models",
    author = "Cabessa, J{\'e}r{\'e}mie  and
      Hernault, Hugo  and
      Mushtaq, Umer",
    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.442/",
    pages = "6624--6635",
    abstract = "An end-to-end argument mining (AM) pipeline takes a text as input and provides its argumentative structure as output by identifying and classifying the argument units and argument relations in the text. In this work, we approach AM using fine-tuned large language models (LLMs). We model the three main sub-tasks of the AM pipeline, as well as their joint formulation, as text generation tasks. We fine-tune eight popular quantized and non-quantized LLMs {--} LLaMA-3, LLaMA-3.1, Gemma-2, Mistral, Phi-3, Qwen-2 {--} which are among the most capable open-weight models, on the benchmark PE, AbstRCT, and CDCP datasets that represent diverse data sources. Our approach achieves state-of-the-art results across all AM sub-tasks and datasets, showing significant improvements over previous benchmarks."
}