🤖 AI Summary
This work addresses the lack of explicit, verifiable reasoning mechanisms in current large language models for debate analysis, which hinders structured representation of support and attack relations among arguments and their collective acceptability. The paper proposes the first unified framework that integrates large language model–driven argument mining, quantitative argumentation semantics, and fuzzy description logic to automatically construct a fuzzy argumentation knowledge base from raw debate texts. By leveraging an efficient query rewriting technique, the framework enables interpretable formal reasoning over this knowledge base. This approach overcomes the black-box limitations of purely statistical models, supports complex semantic queries, and significantly enhances the transparency, verifiability, and logical rigor of computational debate analysis.
📝 Abstract
Large Language Models (LLMs) achieve strong performance in analyzing and generating text, yet they struggle with explicit, transparent, and verifiable reasoning over complex texts such as those containing debates. In particular, they lack structured representations that capture how arguments support or attack each other and how their relative strengths determine overall acceptability. We encompass these limitations by proposing a framework that integrates learning-based argument mining with quantitative reasoning and ontology-based querying. Starting from a raw debate text, the framework extracts a fuzzy argumentative knowledge base, where arguments are explicitly represented as entities, linked by attack and support relations, and annotated with initial fuzzy strengths reflecting plausibility w.r.t. the debate's context. Quantitative argumentation semantics are then applied to compute final argument strengths by propagating the effects of supports and attacks. These results are then embedded into a fuzzy description logic setting, enabling expressive query answering through efficient rewriting techniques. The proposed approach provides a transparent, explainable, and formally grounded method for analyzing debates, overcoming purely statistical LLM-based analyses.