Auto-ARGUE: LLM-Based Report Generation Evaluation

• URL: http://arxiv.org/abs/2509.26184v4
• Date: Fri, 17 Oct 2025 13:06:05 GMT
• Title: Auto-ARGUE: LLM-Based Report Generation Evaluation
• Authors: 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,
• Abstract summary: Auto-ARGUE is a robust implementation of the recently proposed ARGUE framework for report generation evaluation.<n>We present analysis of Auto-ARGUE on the RG pilot task from the TREC 2024 NeuCLIR track, showing good system-level correlations with human judgments.
• Score: 28.307282037395623
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generation of long-form, citation-backed reports is a primary use case for retrieval augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, ones tailored to report generation (RG) are lacking. Accordingly, we introduce Auto-ARGUE, a robust LLM-based implementation of the recently proposed ARGUE framework for RG evaluation. We present analysis of Auto-ARGUE on the RG pilot task from the TREC 2024 NeuCLIR track, showing good system-level correlations with human judgments. We further release a web app for visualization of Auto-ARGUE outputs.

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