CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures
Quick Answer
CAF-Gen is an automated multi-agent framework that enhances shallow argument structures into CAF-compliant models, improving structural integrity through an iterative Creator-Reviewer pipeline.
Quick Take
CAF-Gen is an automated multi-agent framework that enhances shallow argument structures into CAF-compliant models, improving structural integrity through an iterative Creator-Reviewer pipeline. Experiments show that this approach yields higher quality data and better alignment with original annotations compared to single-pass generative models.
Key Points
- CAF-Gen addresses limitations of current Argument Mining techniques in capturing complex reasoning.
- The framework employs a Creator-Reviewer pipeline for iterative validation of argument structures.
- Experiments demonstrate improved data quality and structural richness in argument models.
- Multi-agent collaboration mitigates issues of structural instability in single-pass models.
Article Content
From source RSS / original summaryarXiv:2606. 06646v1 Announce Type: new 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.
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