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arxiv: 2605.13793 · v2 · pith:HVR6JYBJnew · submitted 2026-05-13 · 💻 cs.CL

An LLM-Based System for Argument Mining

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

Pith reviewed 2026-05-20 21:03 UTC · model grok-4.3

classification 💻 cs.CL
keywords argument mininglarge language modelsargument graphsnatural language processingargumentationdirected acyclic graphs
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The pith

An LLM multi-stage pipeline reconstructs text into argument graphs with premises, conclusions, support, attack, and undercut relations.

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

The paper introduces an end-to-end LLM system that breaks argument mining into sequential stages: spotting components in raw text, selecting key elements, and determining their logical connections. These are assembled into directed acyclic graphs using two node types and three relation types. The authors test recovery manually on textbook examples and quantitatively on benchmarks by remapping outputs to match existing annotation schemes. A sympathetic reader cares because the approach promises to scale argument analysis beyond what hand-crafted rules or small models allow. If the pipeline works as described, it would let automated systems extract usable argument structures from large bodies of text without domain-specific retraining.

Core claim

The system processes natural language through a multi-stage pipeline that identifies argumentative components, selects relevant ones, and uncovers logical relations, representing results as directed acyclic graphs consisting of premises and conclusions connected by support, attack, or undercut links. Manual review on textbook arguments shows adequate recovery of structure, while quantitative tests on benchmark datasets yield reasonable performance once outputs are adapted to the datasets' annotation schemes.

What carries the argument

The multi-stage LLM pipeline that progressively identifies argumentative components, selects relevant elements, and uncovers their logical relations to form directed acyclic graphs.

If this is right

  • Argumentative structures can be recovered from textbook-style explanations at usable accuracy.
  • Outputs can be adapted to different annotation schemes while retaining reasonable performance on benchmarks.
  • Directed acyclic graphs with support, attack, and undercut relations become a practical output format for downstream applications.
  • Large-scale processing of natural language text becomes feasible without building new models for each domain.

Where Pith is reading between the lines

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

  • The same pipeline could be applied to legal documents or policy debates to surface hidden support and attack patterns automatically.
  • Combining the extracted graphs with existing reasoning engines might allow machines to simulate or critique entire lines of argument.
  • Domain shifts, such as moving from textbook prose to social media threads, would likely require new mapping rules but could still use the core stages.
  • If the stages prove robust, they offer a template for other structured extraction tasks that currently rely on brittle rule sets.

Load-bearing premise

The pipeline correctly detects components and relations in raw text without systematic LLM errors or mapping artifacts that would make the reported performance invalid.

What would settle it

A hand-checked set of 50 textbook arguments in which the system consistently misses undercut relations or invents unsupported premises would show the recovery claim does not hold.

Figures

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

Figure 1
Figure 1. Figure 1: Overview of the system pipeline. The model converts natural language text into an ar [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
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. 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. 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. 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. 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. 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 mining.

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 / 2 minor

Summary. The paper presents an end-to-end LLM-based multi-stage pipeline for argument mining that extracts premises and conclusions from natural language text, identifies support/attack/undercut relations, and assembles them into directed acyclic graphs. It reports two evaluations: a manual assessment of structure recovery on textbook arguments and a quantitative assessment on benchmark datasets achieved by mapping the system's outputs to existing annotation schemes, claiming adequate recovery and reasonable performance.

Significance. If the mapping from system outputs to benchmark schemes can be shown to be faithful and free of systematic distortion, and if LLM hallucinations in component identification and relation labeling remain low, the work could demonstrate a flexible, scalable alternative to traditional argument mining methods that handles nuanced relations such as undercut.

major comments (2)
  1. [Quantitative evaluation] Quantitative evaluation section: the mapping procedure that converts the system's premises/conclusions plus support/attack/undercut outputs to benchmark annotation schemes (e.g., claim-premise) is not accompanied by explicit rules, worked examples, or inter-annotator checks on the mapped data. Because this step is load-bearing for all reported precision/recall figures, its opacity prevents separation of genuine extraction quality from mapping artifacts.
  2. [Manual evaluation] Manual evaluation section: the textbook-based assessment is described only as showing 'adequate' recovery of argumentative structure, yet no information is given on the number of arguments examined, the precise criteria used to judge recovery, or any measure of inter-rater reliability. This leaves the qualitative claim weakly supported.
minor comments (2)
  1. [Abstract] Abstract: the phrase 'reasonable performance' is used without any numeric anchors; a single sentence summarizing the range of F1 or accuracy values obtained would strengthen the summary.
  2. [System description] Pipeline description: the exact sequence of LLM prompts and any consistency checks between stages are not detailed; adding a figure or pseudocode would clarify how the system avoids inconsistent relation labeling.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and indicate the revisions we will make to improve transparency in both evaluations.

read point-by-point responses

  1. Referee: [Quantitative evaluation] Quantitative evaluation section: the mapping procedure that converts the system's premises/conclusions plus support/attack/undercut outputs to benchmark annotation schemes (e.g., claim-premise) is not accompanied by explicit rules, worked examples, or inter-annotator checks on the mapped data. Because this step is load-bearing for all reported precision/recall figures, its opacity prevents separation of genuine extraction quality from mapping artifacts.

    Authors: We agree that the mapping procedure is critical to interpreting the quantitative results and requires greater transparency. In the revised manuscript, we will add explicit mapping rules, worked examples showing how premises, conclusions, and support/attack/undercut relations are converted to schemes such as claim-premise, and inter-annotator agreement statistics on the mapped outputs to allow readers to distinguish extraction quality from mapping effects. revision: yes

  2. Referee: [Manual evaluation] Manual evaluation section: the textbook-based assessment is described only as showing 'adequate' recovery of argumentative structure, yet no information is given on the number of arguments examined, the precise criteria used to judge recovery, or any measure of inter-rater reliability. This leaves the qualitative claim weakly supported.

    Authors: We acknowledge that the manual evaluation section would benefit from additional detail. We will expand the description to report the number of arguments examined from the textbook, the precise criteria applied to judge recovery of components and relations, and inter-rater reliability measures. This will strengthen the support for our claim of adequate structure recovery. revision: yes

Circularity Check

0 steps flagged

No circularity: evaluation uses external benchmarks and textbook cases

full rationale

The paper presents a multi-stage LLM pipeline for extracting premises, conclusions, and support/attack/undercut relations into DAGs, then evaluates recovery on an argumentation textbook and on benchmark datasets via mapping to established schemes. No equations, fitted parameters, or self-definitional quantities appear. Performance numbers are computed against independent external annotations rather than quantities defined inside the system itself. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work are described in the abstract or evaluation sections. The central claims therefore rest on external data rather than reducing to the paper's own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unexamined assumption that current LLMs can perform reliable component identification and relation labeling when prompted in stages; no new mathematical axioms or invented entities are introduced.

axioms (1)
  • domain assumption Large language models can be prompted to identify argumentative components and relations with sufficient accuracy for the reported tasks.
    Invoked implicitly when the pipeline is presented as a working system without additional verification steps.

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
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    Relation between the paper passage and the cited Recognition theorem.

    multi-stage pipeline that progressively identifies argumentative components, selects relevant elements, and uncovers their logical relations... represented as directed acyclic graphs consisting of two component types (premises and conclusions) and three relation types (support, attack, and undercut)

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uses
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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages · 2 internal anchors

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