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

Researchers introduced MiGUE-Bench, a benchmark for evaluating large language models (LLMs) on multi-granularity event analysis, covering tasks from event detection to future prediction. The benchmark uses an LLM-driven annotation pipeline called MiGUE-Pipeline to generate high-quality labeled data. Experiments on state-of-the-art LLMs and retrieval-augmented generation methods revealed critical deficiencies, highlighting areas for future improvement.

arXiv:2607.27654v1 Announce Type: new 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.