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

arXiv: https://arxiv.org/abs/2603.03095 · HTML: https://mlsanity.com/paper/2603.03095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Exploring the Potential of Large Language Models in Computational Argumentation](https://mlsanity.com/paper/2311.09022/) (2023)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Argument Mining with Structured SVMs and RNNs](https://mlsanity.com/paper/1704.06869/) (2017)
- [Mining Legal Arguments in Court Decisions](https://mlsanity.com/paper/2208.06178/) (2022)
- [Can Large Language Models perform Relation-based Argument Mining?](https://mlsanity.com/paper/2402.11243/) (2024)
- [Efficient argument classification with compact language models and ChatGPT-4 refinements](https://mlsanity.com/paper/2403.15473/) (2024)

## Built on by (most cited first, 1 of 1)

- [Who Argues What? Joint Argument-Entity Detection and Classification in Political Debates](https://mlsanity.com/paper/2609.10192/) (2026)

## Abstract (arXiv)

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.