Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components such as claims and premises. While research on this subtask remains relatively limited compared to other AM tasks, most existing approaches formulate it as a simplified sequence labeling problem, component classification, or a pipeline of component segmentation followed by classification. In this paper, we propose a novel approach based on instruction-tuned Large Language Models (LLMs) using compact instruction-based prompts, and reframe ACD as a language generation task, enabling arguments to be identified directly from plain text without relying on pre-segmented components. Experiments on standard benchmarks show that our approach achieves higher performance compared to state-of-the-art systems. To the best of our knowledge, this is one of the first attempts to fully model ACD as a generative task, highlighting the potential of instruction tuning for complex AM problems.

翻译：论证成分检测是论证挖掘的核心子任务，也是最具挑战性的方面之一，因为它需要联合界定论证片段并将其分类为诸如主张和前提等成分。尽管与其他论证挖掘任务相比，该子任务的研究仍相对有限，但现有方法大多将其简化为序列标注问题、成分分类问题，或采用成分分割后分类的流水线框架。本文提出一种基于指令调优大语言模型的新方法，利用紧凑的指令式提示，将论证成分检测重构为语言生成任务，从而能够直接从纯文本中识别论证，而无需依赖预分割的成分。在标准基准测试上的实验表明，相较于最先进的系统，我们的方法取得了更高的性能。据我们所知，这是首次将论证成分检测完全建模为生成任务的尝试之一，凸显了指令调优在处理复杂论证挖掘问题上的潜力。