Large Language Models (LLMs) demonstrate significant capabilities in
processing natural language data, promising efficient knowledge extraction from
diverse textual sources to enhance situational awareness and support
decision-making. However, concerns arise due to their susceptibility to
hallucination, resulting in contextually inaccurate content. This work focuses
on harnessing LLMs for automated Event Extraction, introducing a new method to
address hallucination by decomposing the task into Event Detection and Event
Argument Extraction. Moreover, the proposed method integrates dynamic
schema-aware augmented retrieval examples into prompts tailored for each
specific inquiry, thereby extending and adapting advanced prompting techniques
such as Retrieval-Augmented Generation. Evaluation findings on prominent event
extraction benchmarks and results from a synthesized benchmark illustrate the
method's superior performance compared to baseline approaches.

本研究聚焦于利用大型语言模型（LLMs）进行自动事件提取，介绍了一种新方法以解决幻觉问题，通过将任务分解为事件检测和事件参数提取，并将动态结构感知的增强检索示例集成到为每个具体查询定制的提示中，从而扩展和适应检索增强生成等先进提示技术。评估结果表明，与基线方法相比，该方法在突出的事件提取基准和合成基准测试中表现出卓越的性能。

基于模型预测的有意识事件提取：分解、增强和提取模式

Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs

Automated event extraction in social science applications often requires
corpus-level evaluations: for example, aggregating text predictions across
metadata and unbiased estimates of recall. We combine corpus-level evaluation
requirements with a real-world, social science setting and introduce the
IndiaPoliceEvents corpus--all 21,391 sentences from 1,257 English-language
Times of India articles about events in the state of Gujarat during March 2002.
Our trained annotators read and label every document for mentions of police
activity events, allowing for unbiased recall evaluations. In contrast to other
datasets with structured event representations, we gather annotations by posing
natural questions, and evaluate off-the-shelf models for three different tasks:
sentence classification, document ranking, and temporal aggregation of target
events. We present baseline results from zero-shot BERT-based models fine-tuned
on natural language inference and passage retrieval tasks. Our novel
corpus-level evaluations and annotation approach can guide creation of similar
social-science-oriented resources in the future.

本文针对社会科学应用中自动化事件提取依赖于数据集层面的评估，提出了一种具有现实意义的印度警务事件语料库，基于这个语料库通过自然问答方式进行标注并评估现成模型在句子分类、文档排序和时间聚合三个任务上的性能表现。通过这种全新的数据集评估和标注方法，可以为未来类似的社会科学语料库资源的创造指明方向。