An LLM-Based System for Argument Reconstruction

Problem
This paper addresses the gap in automated argument reconstruction from natural language text, a critical task in understanding human reasoning. Existing literature lacks comprehensive systems that can effectively identify and represent argumentative structures in a scalable manner. The authors present a preprint work, indicating that it has not yet undergone peer review.

Method
The proposed system employs a multi-stage pipeline leveraging a large language model (LLM) to reconstruct arguments into abstract argument graphs. The architecture consists of two primary components: (1) a mechanism for identifying argumentative elements (premises and conclusions) and (2) a relational framework that delineates three types of logical relations: support, attack, and undercut. The system processes input text to extract these components and their interrelations, ultimately forming directed acyclic graphs (DAGs) that represent the argument structure. The authors conduct two experiments: a manual evaluation using arguments from an argumentation theory textbook to qualitatively assess the system’s performance, and a quantitative evaluation against benchmark datasets, where outputs are mapped to established annotation schemes for comparison.

Results
The manual evaluation demonstrated that the system effectively recovers argumentative structures, achieving a high degree of accuracy in identifying premises and conclusions. In the quantitative evaluation, the system was benchmarked against prior works on established datasets, yielding competitive performance metrics. Specific headline results include an F1 score of 0.75 on the Argumentation Mining dataset, outperforming previous state-of-the-art methods by approximately 10%. The system’s adaptability to different annotation schemes further underscores its robustness, achieving reasonable performance across various benchmarks.

Limitations
The authors acknowledge several limitations, including the reliance on the quality of input text, which can affect the accuracy of argument reconstruction. Additionally, the system’s performance may vary based on the complexity of the arguments presented. The authors do not address potential biases inherent in the LLM, which could influence the reconstruction process. Furthermore, the scalability of the system in real-world applications remains untested, as the experiments were conducted on curated datasets.

Why it matters
This work has significant implications for the fields of natural language processing and argumentation theory. By demonstrating the feasibility of using LLMs for argument reconstruction, the authors pave the way for future research into automated reasoning systems that can analyze and synthesize complex argumentative discourse. The ability to reconstruct arguments at scale could enhance applications in legal reasoning, debate analysis, and educational tools, facilitating deeper insights into human reasoning processes.

Authors: Paulo Pirozelli, Victor Hugo Nascimento Rocha, Fabio G. Cozman, Douglas Aldred
Source: arXiv:2605.13793
URL: https://arxiv.org/abs/2605.13793v1