CAF-Gen: A Multi-Agent System for Enriching Argumentation Structures

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

CAF-Gen is an automated multi-agent framework designed to enrich shallow argument structures from natural text into complex, CAF-compliant argument models. It addresses limitations of current Argument Mining (AM) techniques that struggle with advanced schemas like the Carneades Argumentation Framework (CAF), which incorporates premise types, proof standards, and argument schemes. The system employs an iterative Creator-Reviewer pipeline, where a Creator agent's output is validated by a critical Reviewer agent to ensure structural integrity and semantic richness. Experiments using Google's Gemini 2.5 Pro on the UKP Argument Annotated Essays v2 corpus demonstrated the pipeline's effectiveness. The first-pass acceptance rate of models was 34.6%, which rose significantly to 91.3% after refinement, with an average of 2.35 iterations. Common issues included Structural Inconsistency (44.7%) and Inappropriate Argument Scheme Selection (38.9%). The enriched models showed high fidelity to the source, achieving 99.8% Precision, 99.8% Recall, and 99.3% F1-score for Component Identification, and 67.1% Precision, 99.1% Recall, and 80.0% F1-score for Relation Identification, indicating structural preservation and valuable enrichments.

Key takeaway

For NLP Engineers developing advanced argumentation systems, CAF-Gen demonstrates a robust approach to formalizing natural language arguments. You should consider implementing multi-agent LLM architectures with iterative Creator-Reviewer pipelines to overcome single-pass generation limitations and ensure high-quality, structurally valid outputs. This method significantly boosts model acceptance rates and fidelity, enabling richer analysis in domains like legal reasoning or academic debate.

Key insights

Multi-agent LLM systems with iterative Creator-Reviewer pipelines reliably enrich shallow argument structures into complex formal models.

Principles

• Iterative refinement significantly improves LLM output quality.
• Decomposing tasks into agent roles enhances quality control.
• Formal argumentation benefits from structured LLM-driven enrichment.

Method

CAF-Gen uses a Creator LLM to generate CAF-compliant models from basic argument annotations. A Reviewer LLM iteratively validates and suggests refinements until acceptance or iteration limit is reached.

In practice

• Implement a Creator-Reviewer loop for complex LLM generation tasks.
• Use specific LLM agents for structural validation and semantic checks.
• Apply graph transformation to enrich existing data with new attributes.

Topics

• Argument Mining
• Multi-Agent Systems
• Large Language Models
• Carneades Argumentation Framework
• Formal Argumentation
• Iterative Refinement

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.