Discusses argumentative component detection (ACD) in argument mining as a challenging task, with existing methods simplifying to labeling or pipelines.
Argumentative component detection (ACD) is a core and challenging subtask in Argumentation Mining (AM), requiring the joint identification and classification of argumentative spans such as claims and premises within text . Traditional approaches often simplify ACD into sequence labeling problems, component classification, or multi-stage pipelines that first segment and then classify components, typically relying on pre-segmented units . These methods, while effective in certain contexts, assume accurate prior segmentation—an assumption that limits their applicability in real-world scenarios where argument boundaries are not explicitly marked .
Recent research proposes a novel approach by reframing ACD as a language generation task using instruction-tuned Large Language Models (LLMs) with compact prompting . This method enables direct detection of argumentative components from raw, unsegmented text by prompting the LLM to reproduce the input text while inserting XML-style tags that demarcate both the boundaries and types of components (e.g., claims and premises) . This generative formulation allows for joint segmentation and classification within a unified framework, avoiding error propagation inherent in pipeline models .
Experiments show that this compact prompting approach achieves superior performance, with the best model reaching a macro-F1 score of 0.8778, surpassing existing state-of-the-art systems . Notably, the instruction-tuned LLMs occasionally correct annotation inconsistencies and identify plausible but unannotated argumentative spans, suggesting their potential to improve data quality and interpretative nuance in AM . This work represents one of the first attempts to fully model ACD as a generative task, highlighting the promise of instruction tuning for complex natural language understanding challenges in AI and NLP research
This paper addresses the complexities of Argumentative Component Detection (ACD), a fundamental task in argument mining that involves identifying and classifying argumentative spans (such as claims and premises) within text. The authors critique existing methodologies, noting that prior work often simplifies ACD to a token-level sequence labeling problem or relies on fragile multi-stage pipelines. These traditional approaches frequently struggle with long-range dependencies and error propagation, failing to capture the full semantic context of argumentative structures. To overcome these limitations, the study explores the capabilities of Instruction-tuned Large Language Models (LLMs), proposing a shift from specialized fine-tuned architectures to leveraging the inherent reasoning abilities of generative models through prompting.
The key contribution of this work is the introduction of a Compact Prompting framework designed to perform joint detection and classification efficiently. Unlike standard prompting techniques that can be verbose and computationally expensive, this framework condenses the task definition and instructions to minimize token usage while maximizing performance. By formulating the task as a structured generation problem constrained by specific instructions, the method allows the LLM to identify component boundaries and assign argumentative roles simultaneously. The empirical results demonstrate that this compact, instruction-based approach significantly outperforms conventional fine-tuning baselines (such as BERT-based models) and standard few-shot prompting methods across multiple benchmark datasets.
This research matters because it validates that instruction-tuned LLMs, when guided by efficient prompting strategies, can effectively replace complex, task-specific pipelines in structured prediction tasks. The reduction in prompt length not only lowers computational costs—a critical factor for scaling LLM applications—but also mitigates the "lost in the middle" phenomenon often seen with longer contexts. By achieving superior performance without task-specific fine-tuning, this approach offers a more flexible and robust pathway for deploying argument mining systems in low-resource scenarios or across diverse domains.
This paper addresses argumentative component detection (ACD) in argument mining, a task that aims to identify and classify key elements of an argument (e.g., premises, conclusions, and relations) from unstructured text. Traditional approaches often simplify ACD into isolated labeling tasks or multi-stage pipelines, which can introduce inefficiencies and errors due to sequential dependencies. The authors propose a novel compact prompting framework leveraging instruction-tuned large language models (LLMs) to perform joint detection of all argumentative components in a single inference pass. By formulating ACD as a structured prediction problem and encoding task-specific instructions into prompts, their method achieves competitive performance while reducing computational overhead.
The key contributions include: 1. Unified Prompting for Joint ACD: Unlike prior work, which treats component detection as separate tasks, this approach uses a single prompt to guide the LLM in identifying premises, conclusions, and their relationships simultaneously. 2. Instruction Tuning for Specialized ACD: The method fine-tunes LLMs on argumentative structured instructions, enabling them to handle domain-specific nuances without heavy task-specific training. 3. Empirical Validation: Experiments on benchmark datasets demonstrate that the proposed approach outperforms or matches state-of-the-art pipeline-based methods while being more efficient.
This work is significant because it simplifies the ACD pipeline, making argument mining more scalable and robust for real-world applications like legal reasoning, debate analysis, and persuasive text evaluation. By leveraging the zero/few-shot capabilities of instruction-tuned LLMs, the approach reduces reliance on hand-crafted features and complex post-processing, paving the way for more flexible and generalizable argument mining systems.
Source: [arXiv:2603.03095](https://arxiv.org/abs/2603.03095)