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arXiv · 2509.26184

Auto-ARGUE: LLM-Based Report Generation Evaluation

William Walden·Marc Mason·Orion Weller·Laura Dietz·John Conroy·Neil Molino·Hannah Recknor·Bryan Li·Gabrielle Kaili-May Liu·Yu Hou·Dawn Lawrie·James Mayfield·Eugene Yang
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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.

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William Walden, Marc Mason, Orion Weller, Laura Dietz, John Conroy, Neil Molino, Hannah Recknor, Bryan Li, Gabrielle Kaili-May Liu, Yu Hou, Dawn Lawrie, James Mayfield, Eugene Yang. 2025-09-30. Auto-ARGUE: LLM-Based Report Generation Evaluation. https://arxiv.org/abs/2509.26184

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