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Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection

arXiv:2603.03095v1 Announce Type: new 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 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 know

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Sofiane Elguendouze, Erwan Hain, Elena Cabrio, Serena Villata · · 1 min read · 4 views
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arXiv:2603.03095v1 Announce Type: new 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 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.

Executive Summary

The article proposes a novel approach to argumentative component detection (ACD) using instruction-tuned Large Language Models (LLMs) and compact instruction-based prompts. By reframing ACD as a language generation task, the approach achieves higher performance compared to state-of-the-art systems on standard benchmarks. This method enables arguments to be identified directly from plain text without relying on pre-segmented components, highlighting the potential of instruction tuning for complex Argument(ation) Mining (AM) problems.

Key Points

  • ▸ Novel approach to ACD using instruction-tuned LLMs
  • ▸ Reframing ACD as a language generation task
  • ▸ Higher performance compared to state-of-the-art systems

Merits

Improved Performance

The proposed approach achieves higher performance compared to existing state-of-the-art systems, demonstrating its effectiveness in ACD tasks.

Simplified Process

By reframing ACD as a language generation task, the approach eliminates the need for pre-segmented components, simplifying the argument detection process.

Demerits

Limited Context

The article focuses on ACD in a specific context, which may limit the generalizability of the approach to other AM tasks or domains.

Dependence on LLMs

The approach relies heavily on the performance of instruction-tuned LLMs, which may be affected by factors such as data quality and model bias.

Expert Commentary

The proposed approach represents a significant advancement in ACD, leveraging the capabilities of instruction-tuned LLMs to improve performance and simplify the argument detection process. However, further research is needed to address potential limitations, such as the dependence on LLMs and limited context. The approach has important implications for various applications, including debate analysis, opinion mining, and policy-making, highlighting the need for continued innovation in AM and NLP.

Recommendations

  • ✓ Further research on the generalizability of the approach to other AM tasks and domains
  • ✓ Investigation into the potential applications of the proposed approach in real-world scenarios, such as policy-making and decision-support systems

Sources

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