From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models

• URL: http://arxiv.org/abs/2607.27654v1
• Date: Thu, 30 Jul 2026 04:06:55 GMT
• Title: From Single- to Cross-Document: Benchmarking Multi-Granularity Event Analysis of Large Language Models
• Abstract summary: We introduce MiGUE-Bench, a systematic benchmark for assessing the performance of large language models (LLMs) in event analysis tasks.<n>We first develop an LLM-driven self-correcting annotation framework called MiGUE-Pipeline, enabling scalable acquisition of high-quality source data of events.<n>Then, we design four core tasks in our benchmark, i.e., event detection, relation reasoning, structure induction, and future prediction, to probe model competence at different levels.
• Score: 27.87668856281766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks. To address these limitations, we introduce MiGUE-Bench, a systematic benchmark for assessing the performance of LLMs in multi-granularity event analysis. To support large-scale evaluation, we first develop an LLM-driven self-correcting annotation framework called MiGUE-Pipeline, enabling scalable acquisition of high-quality source data of events with automatic labels. Then, we design four core tasks in our benchmark, i.e., event detection, relation reasoning, structure induction, and future prediction, to probe model competence at different levels, from atomic event details to complex cross-document narratives. Extensive experiments on state-of-the-art LLMs and retrieval-augmented generation (RAG) methods delineate the current capability boundary and identify critical deficiencies, providing insights into the future improvement of LLMs in challenging event analysis tasks.

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