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This paper introduces MiGUE-Bench, a comprehensive benchmark for evaluating large language models (LLMs) on multi-granularity event analysis, addressing gaps in current benchmarks related to document granularity and task design. The authors develop the MiGUE-Pipeline, an LLM-driven self-correcting annotation framework that facilitates the scalable acquisition of high-quality event data with automatic labels. Through extensive experiments across four core tasks鈥攅vent detection, relation reasoning, structure induction, and future prediction鈥攖he study reveals significant performance limitations of state-of-the-art LLMs, highlighting areas for future improvement in event analysis capabilities.
Current LLMs struggle with multi-granularity event analysis, revealing critical performance gaps that could hinder their application in complex narrative tasks.
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 the future improvement of LLMs in challenging event analysis tasks.
Tsinghua AI