From Argument Components to Graphs: A Multi-Agent Debate with Confidence Gating for Argument Relations
arXiv:2606.16047v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly assessed and utilized in the field of Argument Mining (AM), thanks to their strong general reasoning capabilities. However, standard training-free models often miss sophisticated details, specifically in contexts where two parts of the text have to be analyzed together. Furthermore, self-correction mechanisms tend to reinforce initial hallucinations in reasoning. Overcoming these limitations typically requires expensive, domain-specific supervised fine-tuning. Recent work has shown that a multi-agent
The paper leverages the increasing awareness of LLM limitations, particularly in complex reasoning and self-correction, alongside recent advancements in multi-agent systems to propose a new, more robust approach.
This research addresses fundamental reliability issues in LLM-powered applications used for complex cognitive tasks, directly impacting the trust and utility of AI in critical analysis and decision-making.
This paper presents a methodological advancement in making LLMs more reliable for intricate tasks like argument mining, moving beyond simple prompt engineering to a more sophisticated, agent-based reasoning framework.
- · AI researchers
- · NLP developers
- · Organizations relying on argument mining
- · AI ethics and safety advocates
- · Developers of simple single-LLM solutions
- · Methods relying solely on prompt engineering
Improved accuracy and reliability of AI systems in complex analytical tasks like legal review or intelligence analysis.
Accelerated adoption of AI agents for white-collar workflows, as trust in their reasoning capabilities increases.
Shift in AI development towards multi-agent architectures and advanced confidence gating mechanisms as standard practice for robust AI.
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