Auto-ARGUE: LLM-Based Report Generation Evaluation

Bryan Li; Dawn Lawrie; Eugene Yang; Gabrielle Kaili-May Liu; Hannah Recknor; James Mayfield; John Conroy; Laura Dietz; Marc Mason; Neil Molino

arxiv: 2509.26184 · v5 · submitted 2025-09-30 · 💻 cs.IR · cs.AI· cs.CL

Auto-ARGUE: LLM-Based Report Generation Evaluation

William Walden , Marc Mason , Orion Weller , Laura Dietz , John Conroy , Neil Molino , Hannah Recknor , Bryan Li

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Gabrielle Kaili-May Liu Yu Hou Dawn Lawrie James Mayfield Eugene Yang

This is my paper

Pith reviewed 2026-05-18 12:11 UTC · model grok-4.3

classification 💻 cs.IR cs.AIcs.CL

keywords report generation evaluationRAG evaluationLLM-based evaluationcitation-backed reportsTREC NeuCLIRTREC RAGAuto-ARGUEARGUE framework

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The pith

Auto-ARGUE uses LLMs to evaluate citation-backed reports and shows good agreement with human judgments on TREC tasks.

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

The paper presents Auto-ARGUE, an automated system that applies large language models to score and judge reports generated with retrieval augmentation. It tests this approach on report generation from the TREC 2024 NeuCLIR track and two tasks from the TREC 2024 RAG track. A sympathetic reader would care because evaluating such reports by hand is expensive, and a working automatic method would let researchers compare more systems quickly. The work also provides a visualization tool to inspect the automatic scores in detail.

Core claim

We introduce Auto-ARGUE as a robust LLM-based implementation of the ARGUE framework for evaluating citation-backed report generation. When applied to the report generation pilot task from the TREC 2024 NeuCLIR track and two tasks from the TREC 2024 RAG track, it produces system-level scores that correlate well with human judgments. The authors also release ARGUE-Viz, a web application for visualizing and analyzing the judgments and scores produced by Auto-ARGUE.

What carries the argument

Auto-ARGUE, an LLM-based implementation of the ARGUE framework that produces judgments and scores for generated reports.

If this is right

Researchers can evaluate new report generation systems without needing large numbers of human annotators for each comparison.
System rankings from Auto-ARGUE can guide development of better RAG methods for report generation.
The visualization tool allows detailed inspection of where automatic judgments agree or differ from humans.
Evaluation can be repeated quickly as new models or retrieval methods are developed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Extending this to more tasks could help standardize automatic evaluation across different RAG applications.
If the correlations remain stable across different LLMs, it might reduce the cost of running large evaluation campaigns.
Future work could test whether the method works on reports in languages other than English or on different domains.

Load-bearing premise

LLM-generated judgments act as a reliable proxy for human judgments of report quality without adding new systematic biases.

What would settle it

Running human evaluations on the same TREC report outputs and finding that the system-level correlations with Auto-ARGUE scores drop below acceptable levels would falsify the main claim.

read the original abstract

Generation of citation-backed reports is a primary use case for retrieval-augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, tools designed for report generation are lacking. Accordingly, we introduce Auto-ARGUE, a robust LLM-based implementation of the recently proposed ARGUE framework for report generation evaluation. We present analysis of Auto-ARGUE on the report generation pilot task from the TREC 2024 NeuCLIR track and on two tasks from the TREC 2024 RAG track, showing good system-level correlations with human judgments. Additionally, we release ARGUE-Viz, a web app for visualization and fine-grained analysis of Auto-ARGUE judgments and scores.

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

Auto-ARGUE ships a usable open-source LLM evaluator for citation-backed reports plus a viz app, with reported system-level correlations to TREC human judgments, though aggregate numbers leave room for shared bias questions.

read the letter

The punchline here is that the authors have created and open-sourced Auto-ARGUE, an LLM implementation of the ARGUE framework for assessing citation-backed reports from RAG systems, along with a visualization app, and they demonstrate reasonable system-level correlations with human judgments on TREC 2024 tasks. They do a good job filling a practical need. Evaluation for short-form RAG answers has tools, but report generation with citations has been underserved. By implementing the framework and testing on the NeuCLIR report pilot and RAG track tasks using external human judgments, they provide something developers can use right away. The ARGUE-Viz app adds value by allowing fine-grained looks at the scores, which helps in understanding where the evaluator agrees or disagrees with humans. Where it is softer is in the reliance on aggregate correlations. System-level Pearson or Spearman numbers can be inflated if the evaluator LLM has overlapping weaknesses with the systems it is scoring, such as similar handling of citations or factual consistency. The concern from the stress test about shared biases is a fair one to raise, and it would be stronger if the paper included more on the prompting strategy or breakdowns of individual report scores. Still, using TREC data as the benchmark is a positive choice that avoids circularity. This paper targets researchers and engineers building RAG systems for report-style outputs. Readers who need an off-the-shelf evaluator for citation quality will find it useful, even if it is more of an engineering contribution than a theoretical one. It is worth a serious referee because the new artifact and the reported results on public tasks make it relevant for the field to review and potentially improve upon. I would recommend engaging with the work in peer review.

Referee Report

1 major / 1 minor

Summary. The manuscript introduces Auto-ARGUE, a robust LLM-based implementation of the ARGUE framework for evaluating citation-backed report generation in RAG systems. It reports good system-level correlations with human judgments on the TREC 2024 NeuCLIR report generation pilot task and two tasks from the TREC 2024 RAG track, and releases the ARGUE-Viz web app for visualization and fine-grained analysis of judgments.

Significance. If the correlations prove robust, Auto-ARGUE would fill a noted gap in open-source tools for report-generation evaluation and provide a practical, reproducible resource via the released implementation and visualization app. The work directly supports community efforts to scale evaluation of citation-backed outputs in retrieval-augmented generation.

major comments (1)

The central claim that Auto-ARGUE judgments serve as a reliable proxy rests on the system-level correlations reported in the experimental analysis. These correlations alone do not rule out the possibility that shared training-data or prompting artifacts between the evaluator LLM and the tested RAG systems inflate agreement on factuality and citation quality; additional controls or per-instance disagreement analysis would be required to secure the claim.

minor comments (1)

The implementation section would benefit from explicit listing of the prompt templates, model identifier, temperature, and few-shot examples used in Auto-ARGUE to support reproducibility and external bias audits.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The suggestion to further secure the claim regarding Auto-ARGUE as a reliable proxy is well-taken, and we address it directly below.

read point-by-point responses

Referee: The central claim that Auto-ARGUE judgments serve as a reliable proxy rests on the system-level correlations reported in the experimental analysis. These correlations alone do not rule out the possibility that shared training-data or prompting artifacts between the evaluator LLM and the tested RAG systems inflate agreement on factuality and citation quality; additional controls or per-instance disagreement analysis would be required to secure the claim.

Authors: We agree that system-level correlations, while informative for ranking systems, do not by themselves fully rule out potential artifacts from shared training data or prompting strategies. To strengthen the evidence, we will incorporate a per-instance disagreement analysis in the revised manuscript. This analysis will examine specific cases of divergence between Auto-ARGUE judgments and human assessments on factuality and citation quality, including qualitative review of disagreement patterns. We note that the TREC 2024 submissions involve diverse RAG systems from multiple teams using varied underlying models, which reduces (but does not eliminate) the risk of systematic overlap with the evaluator LLM; the added per-instance analysis will help address this concern more directly. revision: yes

Circularity Check

0 steps flagged

No circularity: correlations computed directly against external TREC human judgments

full rationale

The paper presents Auto-ARGUE as an LLM implementation of the ARGUE framework and reports system-level Pearson/Spearman correlations with human judgments on the TREC 2024 NeuCLIR report generation pilot and two RAG tasks. These benchmarks originate from independent TREC organizers and human assessors, external to any parameters, prompts, or data fitted inside the present work. No equations, self-definitional loops, or load-bearing self-citations are invoked to derive the reported numbers; the results are straightforward empirical comparisons. The additional release of ARGUE-Viz is a visualization tool and does not alter the evaluation chain. The derivation is therefore self-contained and falsifiable against the cited external human data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the ARGUE framework (assumed from prior work) and the assumption that LLMs can faithfully execute its evaluation criteria without additional calibration.

axioms (1)

domain assumption LLM outputs can be used to implement the ARGUE framework judgments reliably
Invoked when presenting Auto-ARGUE as a robust implementation that correlates with humans.

pith-pipeline@v0.9.0 · 5685 in / 1359 out tokens · 34338 ms · 2026-05-18T12:11:57.348271+00:00 · methodology

discussion (0)

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