With the emergence of large language models (LLMs), there is an expectation that LLMs can effectively extract explicit information from complex real-world documents (e.g., papers, reports). However, most LLMs generate paragraph-style answers that are chaotic, disorganized, and untraceable. To bridge this gap, we introduce the Arranged and Organized Extraction Benchmark (AOE), a new bilingual benchmark with data and documents of varying lengths designed to systematically evaluate the ability of LLMs to comprehend fragmented documents and reconstruct isolated information into one organized table. Unlike conventional text-to-table tasks, which rely on fixed schema and narrow task domains, AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries. In the experiment, we evaluated both open-source and closed-source state-of-the-art LLMs. The results show that even the most advanced models struggled significantly. The benchmark is available at https://anonymous.4open.science/r/AOE-Benchmark/.
@article{arxiv.2507.16271,
title = {Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction},
author = {Tianyun Zhong and Guozhao Mo and Yanjiang Liu and Yihan Chen and Lingdi Kong and Xuanang Chen and Yaojie Lu and Hongyu Lin and Shiwei Ye and Xianpei Han and Ben He and Le Sun},
journal= {arXiv preprint arXiv:2507.16271},
year = {2025}
}