CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
Formalizing complex reasoning from natural text is a central challenge in computational linguistics. Current Argument Mining techniques identify basic claims and premises but struggle with richer structures required by advanced schemas like the Carneades Argumentation Framework (CAF). We introduce CAF-Gen, an automated multi-agent framework that enriches shallow argument structures into CAF-compliant models using an iterative Creator-Reviewer pipeline. Experiments show the iterative feedback loop improves data quality and achieves strong alignment with original annotations.
[2606.06646] CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
[Submitted on 4 Jun 2026]
Title:CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
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Abstract:Formalizing complex reasoning from natural text is one of the central challenges in computational linguistics. It requires systems to understand not just keywords but also the context and complex reasoning embedded in a text. Current Argument Mining (AM) techniques identify basic claims and premises, yet they often struggle to capture the richer structural information required by advanced schemas such as the Carneades Argumentation Framework (CAF), which incorporates features such as premise types, proof standards, and argument schemes. We address this limitation by introducing CAF-Gen, an automated multi-agent framework designed to enrich shallow argument structures into CAF-compliant argument models. By employing an iterative Creator-Reviewer pipeline, a creator agent's output is validated by a critical agent to ensure structural integrity. This multi-agent collaboration is crucial for mitigating the structural instability typical of single-pass generative models. Our experiments demonstrate that the iterative feedback loop improves the quality of the resulting data and achieves strong alignment with the original annotations, while producing structurally richer models. Our findings show that the multi-agent system can overcome the limitations of single-pass generation, providing a robust methodology for the automated modeling of formal argumentation.
Comments: Accepted for publication in the proceedings of ICCCI 2026
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.06646 [cs.CL]
(or arXiv:2606.06646v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.06646
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jakub Bąba [view email] [v1] Thu, 4 Jun 2026 18:51:58 UTC (424 KB)
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