LLM-based Argument Mining meets Argumentation and Description Logics: a Unified Framework for Reasoning about Debates
arXiv:2603.02858v1 Announce Type: new 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
arXiv:2603.02858v1 Announce Type: new 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.
Executive Summary
This article proposes a unified framework that integrates Large Language Models (LLMs) with argumentation and description logics to reason about debates. The framework extracts a fuzzy argumentative knowledge base from raw debate text, applies quantitative argumentation semantics to compute final argument strengths, and embeds these results into a fuzzy description logic setting for efficient query answering. This approach overcomes the limitations of purely statistical LLM-based analyses, providing a transparent, explainable, and formally grounded method for analyzing debates. The framework's ability to capture structured representations of arguments and their relative strengths makes it a valuable tool for various applications, including argument mining, debate analysis, and decision-making support.
Key Points
- ▸ The proposed framework integrates LLMs with argumentation and description logics to reason about debates.
- ▸ The framework extracts a fuzzy argumentative knowledge base from raw debate text.
- ▸ Quantitative argumentation semantics are applied to compute final argument strengths.
- ▸ The results are embedded into a fuzzy description logic setting for efficient query answering.
Merits
Strengths in Argument Representation
The framework provides a structured representation of arguments and their relative strengths, making it a valuable tool for various applications.
Improved Transparency and Explainability
The approach overcomes the limitations of purely statistical LLM-based analyses, providing a transparent and explainable method for analyzing debates.
Formal Grounding
The framework is formally grounded in argumentation theory and description logic, ensuring a solid foundation for reasoning about debates.
Demerits
Limited Scalability
The framework's performance may degrade with increasing debate complexity and size, limiting its scalability for large-scale applications.
Dependence on High-Quality Training Data
The framework's effectiveness relies on the availability of high-quality training data, which can be challenging to obtain and annotate.
Complexity of Implementation
The framework's integration of multiple components, including LLMs, argumentation semantics, and description logics, may result in a complex implementation process.
Expert Commentary
The proposed framework is a significant contribution to the field of argumentation and debate analysis. Its ability to integrate LLMs with argumentation and description logics makes it a valuable tool for various applications. However, its limitations, including scalability and dependence on high-quality training data, need to be addressed. Furthermore, the framework's complexity of implementation may require significant expertise and resources. In conclusion, the proposed framework has the potential to revolutionize debate analysis and decision-making support, but its practical applications and scalability need to be carefully evaluated.
Recommendations
- ✓ Further research is needed to address the framework's limitations, including scalability and dependence on high-quality training data.
- ✓ The framework's complexity of implementation requires significant expertise and resources, making it challenging for widespread adoption.