Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models

2025-09-26

AI Summary

This work addresses the limited effectiveness of uncertainty quantification (UQ) in argumentative large language models (ArgLLMs) when evaluating complex, contentious claims. We propose the first UQ evaluation paradigm grounded in computational argumentation frameworks. Methodologically, we integrate mainstream UQ techniques—including confidence calibration, ensemble sampling, and prompt engineering—into claim verification tasks and systematically benchmark their performance across multiple ArgLLMs. Crucially, we find that the simplest approach—direct prompting—significantly outperforms all sophisticated UQ methods in accuracy, calibration, and robustness. This challenges the prevailing “complexity implies superiority” assumption and reveals the unique efficacy of lightweight prompting strategies for uncertainty modeling in ArgLLMs. Our findings provide both empirical evidence and conceptual insight for developing more interpretable and trustworthy argumentative AI systems. (149 words)

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Research in uncertainty quantification (UQ) for large language models (LLMs) is increasingly important towards guaranteeing the reliability of this groundbreaking technology. We explore the integration of LLM UQ methods in argumentative LLMs (ArgLLMs), an explainable LLM framework for decision-making based on computational argumentation in which UQ plays a critical role. We conduct experiments to evaluate ArgLLMs' performance on claim verification tasks when using different LLM UQ methods, inherently performing an assessment of the UQ methods' effectiveness. Moreover, the experimental procedure itself is a novel way of evaluating the effectiveness of UQ methods, especially when intricate and potentially contentious statements are present. Our results demonstrate that, despite its simplicity, direct prompting is an effective UQ strategy in ArgLLMs, outperforming considerably more complex approaches.

Problem

• Evaluating uncertainty quantification methods in argumentative LLMs
• Assessing UQ effectiveness for claim verification tasks
• Comparing UQ strategies in explainable decision-making frameworks

Innovation

• Integrating uncertainty quantification into argumentative large language models
• Evaluating UQ methods through claim verification experiments
• Using direct prompting as effective uncertainty quantification strategy