arxiv: 2605.13793 · v1 · pith:HVR6JYBJnew · submitted 2026-05-13 · 💻 cs.CL

An LLM-Based System for Argument Reconstruction

Paulo Pirozelli , Victor Hugo Nascimento Rocha , Fabio G. Cozman , Douglas Aldred This is my paper

Pith reviewed 2026-05-14 19:07 UTC · model grok-4.3

classification 💻 cs.CL

keywords argument reconstructionlarge language modelsargument graphsargument miningdirected acyclic graphssupport attack undercut relationsnatural language processing

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The pith

An LLM pipeline reconstructs natural language text into argument graphs with premises, conclusions, and support, attack or undercut relations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes a multi-stage system that uses large language models to convert raw text into structured argument graphs. The pipeline first spots argumentative parts, picks the key ones, and then maps how they connect logically. A reader would care because this automates analysis of reasoning in debates, legal texts, or textbooks that would otherwise require slow manual work. If the approach holds, it shows LLMs can produce usable graphs across different annotation styles without heavy retraining. This opens a path to handling larger volumes of arguments consistently.

Core claim

The end-to-end LLM-based system follows a multi-stage pipeline that progressively identifies argumentative components, selects relevant elements, and uncovers their logical relations. These are represented as directed acyclic graphs consisting of premises and conclusions connected by support, attack, or undercut relations. Manual checks on textbook arguments confirm adequate recovery of structure, while tests on benchmark datasets show reasonable performance once outputs are mapped to existing annotation schemes.

What carries the argument

The multi-stage LLM pipeline that identifies components, selects elements, and determines support, attack, or undercut relations to form directed acyclic argument graphs.

If this is right

Argument reconstruction scales to larger text collections without manual annotation at every step.
The same pipeline can be adapted to match different existing annotation schemes for direct comparison with prior methods.
Graphs with attack and undercut relations become available for downstream tasks such as summarization or evaluation.
Performance holds across benchmark datasets once outputs are aligned to the target scheme.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The graphs could feed into tools that track how arguments evolve across multiple documents or debates.
Further tests on noisy sources such as social media threads would show whether the pipeline needs extra safeguards.
Combining the output graphs with automated evaluation metrics might allow real-time feedback on argument strength.

Load-bearing premise

The multi-stage LLM pipeline reliably identifies argumentative components and their relations without systematic human correction or domain-specific fine-tuning beyond the described prompting.

What would settle it

A fresh manual comparison on 50 textbook arguments where the system outputs are checked against expert annotations and show repeated errors in choosing undercut relations over support or attack.

Figures

Figures reproduced from arXiv: 2605.13793 by Douglas Aldred, Fabio G. Cozman, Paulo Pirozelli, Victor Hugo Nascimento Rocha.

**Figure 2.** Figure 2: Diagram of the Teacher argument. Explicit premises are shown as [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Text: “Nothing is demonstrable, unless the contrary implies a contradiction. Nothing, that [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

**Figure 4.** Figure 4: Text: “Everything that comes into existence has causes different from itself. The universe [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: Text: “Beavers build very complex dams that create large lakes. These dams are built [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Text: “The thieves fled and there are only two paths they could have taken — to the left, [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

**Figure 7.** Figure 7: Mean F1-score as a function of the similarity threshold between predicted and gold com [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗

read the original abstract

Arguments are a fundamental aspect of human reasoning, in which claims are supported, challenged, and weighed against one another. We present an end-to-end large language model (LLM)-based system for reconstructing arguments from natural language text into abstract argument graphs. The system follows a multi-stage pipeline that progressively identifies argumentative components, selects relevant elements, and uncovers their logical relations. These elements are represented as directed acyclic graphs consisting of two component types (premises and conclusions) and three relation types (support, attack, and undercut). We conduct two complementary experiments to evaluate the system. First, we perform a manual evaluation on arguments drawn from an argumentation theory textbook to assess the system's ability to recover argumentative structure. Second, we conduct a quantitative evaluation on benchmark datasets, allowing comparison with prior work by mapping our outputs to established annotation schemes. Results show that the system can adequately recover argumentative structures and, when adapted to different annotation schemes, achieve reasonable performance across benchmark datasets. These findings highlight the potential of LLM-based pipelines for scalable argument reconstruction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents an end-to-end LLM-based multi-stage pipeline for reconstructing arguments from natural language text into directed acyclic graphs consisting of premises and conclusions connected by support, attack, and undercut relations. It evaluates the system through a manual assessment on arguments from an argumentation theory textbook and a quantitative evaluation on benchmark datasets, where outputs are mapped to established annotation schemes. The central claim is that the system adequately recovers argumentative structures and achieves reasonable performance across benchmarks when adapted to different schemes.

Significance. If the reported results hold with sufficient quantitative backing, the work would demonstrate a flexible, prompt-based approach to argument reconstruction that avoids domain-specific fine-tuning and adapts across annotation schemes. This could support scalable applications in computational argumentation, provided the pipeline's reliability is more rigorously quantified.

major comments (2)

[Quantitative evaluation] Quantitative evaluation section: The claim of 'reasonable performance across benchmark datasets' after mapping outputs to prior schemes is stated without any specific metrics (e.g., precision, recall, F1), error analysis, failure mode breakdown, or details on the mapping procedure and prompt engineering choices. This leaves the central performance claim only loosely supported, as noted in the abstract's description of complementary experiments.
[Manual evaluation] Manual evaluation description: The manual recovery assessment on textbook arguments is described only at a high level ('assess the system's ability to recover argumentative structure') without reporting inter-annotator agreement, specific success/failure rates, or examples of recovered graphs versus ground truth. This makes it difficult to evaluate the adequacy claim for the multi-stage pipeline.

minor comments (1)

[Abstract and introduction] The abstract and introduction could more explicitly define the three relation types (support, attack, undercut) and the DAG constraints to improve accessibility for readers unfamiliar with argumentation theory.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and indicate planned revisions to strengthen the quantitative and manual evaluation sections.

read point-by-point responses

Referee: Quantitative evaluation section: The claim of 'reasonable performance across benchmark datasets' after mapping outputs to prior schemes is stated without any specific metrics (e.g., precision, recall, F1), error analysis, failure mode breakdown, or details on the mapping procedure and prompt engineering choices. This leaves the central performance claim only loosely supported, as noted in the abstract's description of complementary experiments.

Authors: We agree that the quantitative evaluation would be strengthened by explicit metrics and supporting details. In the revised manuscript we will add precision, recall, and F1 scores for the mapped benchmark results, include an error analysis and failure-mode breakdown, and expand the description of the mapping procedure and prompt-engineering choices used to adapt outputs to prior annotation schemes. revision: yes
Referee: Manual evaluation description: The manual recovery assessment on textbook arguments is described only at a high level ('assess the system's ability to recover argumentative structure') without reporting inter-annotator agreement, specific success/failure rates, or examples of recovered graphs versus ground truth. This makes it difficult to evaluate the adequacy claim for the multi-stage pipeline.

Authors: The manual evaluation was presented at a high level to illustrate qualitative recovery of argumentative structure. We will revise this section to report inter-annotator agreement, specific success and failure rates, and concrete examples of recovered graphs compared against ground-truth structures from the textbook arguments. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper describes an empirical multi-stage LLM pipeline for argument reconstruction, with manual evaluation on textbook examples and quantitative mapping to external benchmark datasets. No equations, fitted parameters, or derivations appear; the central claims rest on independent performance metrics against established annotation schemes rather than any self-referential reduction or self-citation load-bearing step. The work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the unstated assumption that current LLMs possess sufficient implicit knowledge of argumentative structure to be guided by prompting alone; no free parameters, mathematical axioms, or new invented entities are introduced.

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discussion (0)

Reference graph

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