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LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates

arXiv – CS AI|Gianvincenzo Alfano, Sergio Greco, Lucio La Cava, Stefano Francesco Monea, Irina Trubitsyna|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers have developed a new framework that combines Large Language Models with structured reasoning to analyze debates more transparently. The system extracts arguments from text, maps their relationships, and uses quantitative methods to determine argument strengths, addressing LLMs' limitations in explicit reasoning.

Key Takeaways

→LLMs struggle with transparent and verifiable reasoning over complex debate texts despite strong text analysis capabilities.
→The new framework integrates argument mining with quantitative reasoning and ontology-based querying for structured debate analysis.
→The system extracts fuzzy argumentative knowledge bases with explicit argument entities and attack/support relationships.
→Quantitative argumentation semantics compute final argument strengths by propagating effects of supports and attacks.
→The approach enables expressive query answering through efficient rewriting techniques in a fuzzy description logic setting.

#llm #argument-mining #reasoning #nlp #machine-learning #research #debate-analysis #knowledge-extraction

Read Original →via arXiv – CS AI

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