Abstract
Events refer to specific occurrences, incidents, or happenings that take place under a particular background. Event reasoning aims to infer events according to certain relations and predict future events. The cutting-edge techniques for event reasoning play a crucial role in various natural language processing applications. Large language models (LLMs) have made significant advancements in event reasoning owing to their wealth of knowledge and reasoning capabilities. However, smaller instruction-tuned models currently in use do not consistently demonstrate exceptional proficiency in managing these tasks. This discrepancy arises from the absence of explicit modeling of events and the interconnections of them within their instruction data. Consequently, these models face challenges in comprehending event structures and semantics while struggling to bridge the gap between their interpretations and human understanding of events. Additionally, their limitations in grasping event relations lead to constrained event reasoning abilities to effectively deduce and incorporate pertinent event knowledge. In this paper, we propose Event-Oriented Instruction Tuning (EvIT) to train our LLM. Specifically, we first propose a novel structure named event quadruple which contains the structure and semantics of events and is complete in the event representation. We then design event-relation learning based on the structures. We encapsulate the learning into the instruction-tuning formulation to better stimulate the event reasoning capacity of our model. We design a heuristic unsupervised method to mine event quadruple from a large-scale corpus. At last, we finetune a Llama model on our Event-Oriented Instruction Tuning. We conduct extensive experiments on event reasoning tasks on several datasets. Automatic and human evaluations demonstrate EvIT achieves competitive performances on event reasoning.
Abstract (translated)
事件指的是在特定背景下发生的具体事件、事故或现象。事件推理旨在根据某些关系推断事件并预测未来事件。事件推理的最新技术在各种自然语言处理应用中发挥了关键作用。由于其知识丰富和推理能力，大型语言模型（LLMs）在事件推理方面取得了显著进展。然而，当前使用的较小调整模型在处理这些任务时并没有表现出非凡的熟练程度。这一差异源于事件和它们在指令数据中的相互关系缺乏明确的建模。因此，这些模型在理解和解释事件结构方面遇到了挑战，同时在将它们的解释与人类对事件的认知之间存在差距。此外，它们在理解事件关系方面的限制导致它们无法有效推断和融入相关事件知识。在本文中，我们提出了事件导向指令调整（EvIT）来训练我们的LLM。具体来说，我们首先提出了一个名为事件四元组的全新结构，它包含了事件和事件的表示结构，并且是完整的。然后，我们基于结构设计事件关系学习。我们将学习封装到指令调整公式中，以更好地刺激模型的事件推理能力。我们设计了一个基于节点的未经监督的方法，用于从大型语料库中挖掘事件四元组。最后，我们在事件导向指令调整上对Llama模型进行微调。我们在多个数据集上进行了广泛的实验，自动和人工评估都表明，EvIT在事件推理上取得了竞争力的性能。
URL
https://arxiv.org/abs/2404.11978
