提取论辩要素而非仅分类：用于生成式组件检测的指令微调大语言模型

Computer Science > Artificial Intelligence arXiv:2609.24855 (cs) [Submitted on 21 Sep 2026] Title:Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection Authors:Sofiane Elguendouze (UniCA, I3S, MARIANNE), Erwan Hain (UniCA, MARIANNE), Elena Cabrio (MARIANNE, UniCA), Serena Villata (CNRS, MARIANNE) View a PDF of the paper titled Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection, by Sofiane Elguendouze (UniCA and 8 other authors View PDF Abstract: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 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. Our code and the datasets used are openly available in the following GitHub repository. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2609.24855 [cs.AI] (or arXiv:2609.24855v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.24855 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: COLM 2026 - Third Annual Conference on Language Modeling, Oct 2026, San Francisco, CA, United States Submission history From: Sofiane Elguendouze [view email] [via CCSD proxy] [v1] Mon, 21 Sep 2026 16:30:38 UTC (76 KB) Full-text links: Access Paper: View a PDF of the paper titled Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection, by Sofiane Elguendouze (UniCA and 8 other authors View PDF TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs cs.CL References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)