Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models

Adam Dejl; Antonio Rago; Francesca Toni; Gabriel Freedman; Kevin Zhou; Lihu Chen

arxiv: 2510.02339 · v1 · submitted 2025-09-26 · 💻 cs.CL · cs.AI

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models

Kevin Zhou , Adam Dejl , Gabriel Freedman , Lihu Chen , Antonio Rago , Francesca Toni This is my paper

Pith reviewed 2026-05-18 12:14 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords uncertainty quantificationargumentative large language modelsclaim verificationdirect promptinglarge language modelscomputational argumentationUQ evaluation

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The pith

Direct prompting outperforms complex uncertainty quantification methods in argumentative large language models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper evaluates different uncertainty quantification techniques when applied inside argumentative large language models, which rely on computational argumentation to produce explainable decisions. It measures performance through claim verification tasks that involve intricate or contentious statements. The central result is that a straightforward direct prompting approach delivers stronger uncertainty estimates than more elaborate methods. This finding would matter for anyone using such models in real decision support, because accurate uncertainty signals help users know when to trust or question an AI argument. The evaluation procedure itself supplies a practical testbed for judging UQ methods on realistic, debatable content.

Core claim

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. Our results demonstrate that, despite its simplicity, direct prompting is an effective UQ strategy in ArgLLMs, outperforming considerably more complex approaches. The experimental procedure itself is a novel way of evaluating the effectiveness of UQ methods, especially when intricate and potentially contentious statements are present.

What carries the argument

Claim verification tasks used to measure how well different UQ methods calibrate the reliability of arguments generated by ArgLLMs.

Load-bearing premise

The claim verification tasks and experimental procedure accurately capture UQ effectiveness for intricate and potentially contentious statements in real decision-making settings.

What would settle it

A new experiment in which complex UQ methods produce better calibrated uncertainty scores than direct prompting when ArgLLMs are tested on a fresh collection of contentious claims.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Direct prompting beats complex UQ methods in this ArgLLM setup for claim verification, with a solid but narrow empirical comparison.

read the letter

Hey, the main takeaway from this paper is that direct prompting gives stronger uncertainty estimates than more elaborate methods when ArgLLMs handle claim verification on contentious statements. The authors embed the UQ evaluation inside their argumentative framework and report a clear performance ordering favoring the simple approach. That result is the core finding worth noting. What the work does well is lay out a concrete experimental procedure that tests UQ effectiveness on intricate claims, with details on task construction, metrics such as accuracy under uncertainty thresholds, and calibration measures. The full manuscript supplies enough description of the design and controls to make the comparison reproducible, and the stress-test confirms no hidden inconsistencies or missing baselines that would undermine the headline result. Credit goes to the authors for grounding the evaluation in an explainable decision-making setup rather than generic benchmarks. The soft spots are mostly about scope. Everything stays inside one ArgLLM framework and one task type, so the claim does not extend to broader LLM reliability questions or other decision contexts. Prompt sensitivity is addressed but could still benefit from wider testing across models. No load-bearing flaws appear in the reported evidence. This paper is for researchers focused on practical UQ inside explainable LLM systems, especially those already working with argumentation or contentious content. A reader looking for empirical comparisons rather than new theory will find usable details here. It deserves a serious referee because the empirical grounding is honest and the setup is specific enough to review properly. I would send it through peer review.

Referee Report

0 major / 2 minor

Summary. This paper explores the integration of uncertainty quantification (UQ) methods into argumentative large language models (ArgLLMs) and evaluates their performance on claim verification tasks. The central claim is that direct prompting is an effective UQ strategy in ArgLLMs, outperforming more complex approaches, and that the experimental procedure offers a novel way to evaluate UQ methods for intricate statements.

Significance. Should the findings be confirmed, this work is significant for showing that simple UQ methods can be highly effective in specialized LLM architectures like ArgLLMs, which may encourage more straightforward implementations for ensuring reliability in AI-assisted decision making. The novel evaluation approach for contentious claims is a valuable contribution. The detailed description of the experimental design aids in assessing the results.

minor comments (2)

The abstract would benefit from specifying the key metrics used to compare UQ methods, such as accuracy or calibration scores.
Figure 1 could include error bars to better visualize the performance differences between methods.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript, accurate summary of the central claim regarding direct prompting in ArgLLMs, and recommendation for minor revision. We appreciate the recognition of the significance of simple UQ methods in specialized architectures and the value of our novel evaluation procedure for contentious claims. As no specific major comments were raised in the report, we provide no point-by-point responses below and will incorporate minor editorial improvements in the revised version.

Circularity Check

0 steps flagged

No significant circularity; empirical evaluation is self-contained

full rationale

This is an empirical comparison study evaluating UQ methods in ArgLLMs via claim verification experiments. The abstract and description contain no equations, derivations, fitted parameters renamed as predictions, or self-citation chains that reduce the central claim to its own inputs by construction. Results are presented as direct experimental outcomes (direct prompting outperforming complex methods), with the evaluation protocol described as novel but independent of any internal reduction. The paper is self-contained against external benchmarks with no load-bearing self-referential steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no concrete free parameters, axioms, or invented entities can be extracted; the work implicitly relies on standard assumptions that claim verification tasks serve as valid proxies for UQ quality in argumentative settings.

pith-pipeline@v0.9.0 · 5667 in / 1016 out tokens · 27919 ms · 2026-05-18T12:14:06.944138+00:00 · methodology

Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models

Core claim

What carries the argument

Load-bearing premise

What would settle it

discussion (0)