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CAF-Gen is a multi-agent LLM-driven framework that enriches shallow argument structures into formal Carneades Argumentation Framework models using an iterative Creator-Reviewer pipeline, achieving improved structural alignment and quality.
This paper presents a fully automated pipeline that transforms court decisions into legal commentaries by extracting, clustering, and summarizing paragraph-level chunks using LLMs, evaluated on German civil code cases.
This paper introduces TIDE, a novel framework that integrates trial and debate mechanisms to improve criteria-based prompt optimization for argumentative essay understanding tasks such as automated essay scoring, argument component detection, and argument relation identification. Experiments show performance improvements, highlighting the potential of combining prompt-based methods for robust argument analysis.
Researchers from the University of British Columbia propose an unsupervised graph-based system for organizing arguments from online debates by constructing interaction graphs and applying community detection to reveal diverse viewpoint distributions. The approach requires no training data and aims to help users navigate complex argumentative landscapes and combat filter bubbles.