Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection· 论据组件检测的新方法
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 ITFACD, 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
利用指令调优大模型实现论据组件直接生成检测。
- 核心方法
- 提出ITFACD方法，基于指令调优的大型语言模型，通过简洁的指令提示将论据组件检测重构为语言生成任务，直接从文本中生成并识别论据。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 论据组件检测任务中，现有方法通常简化为序列标注或组件分类，无法有效同时完成论据边界确定和分类。
- 关键实验
- 在多个数据集上进行了实验，展示该方法相比传统方法在性能上的提升，但具体数据未提供。
- 主要贡献
- 创新性地将论据组件检测视为语言生成任务，提高了检测的准确性和效率，减少对预分割文本的依赖。
- 意义与局限
- 该研究对论据挖掘领域有重要影响，为解决论据组件检测的挑战提供了新思路，但也存在对大模型资源需求较高的局限。