EVIT：面向事件推理的事件导向指令微调
EVIT: Event-Oriented Instruction Tuning for Event Reasoning
AI总结：
针对小型指令微调模型事件推理能力不足的问题，提出事件导向指令微调（EvIT），通过事件四元组结构和关系学习增强模型的事件理解与推理，并在多个数据集上取得竞争性性能。
摘要：
事件是指在特定背景下发生的具体事件、事故或情况。事件推理旨在根据特定关系推断事件并预测未来事件。最先进的事件推理技术在各种自然语言处理应用中发挥着至关重要的作用。大型语言模型（LLMs）凭借其丰富的知识和推理能力，在事件推理方面取得了显著进展。然而，目前使用的小型指令微调模型在处理这些任务时并未持续表现出卓越的能力。这种差异源于其指令数据中缺乏对事件及其相互关联的显式建模。因此，这些模型在理解事件结构和语义方面面临挑战，难以弥合其解释与人类对事件理解之间的差距。此外，它们在把握事件关系方面的局限性导致事件推理能力受限，无法有效推断和整合相关事件知识。在本文中，我们提出了事件导向指令微调（EvIT）来训练我们的LLM。具体来说，我们首先提出了一种名为事件四元组的新结构，该结构包含事件的结构和语义，并且在事件表示上是完整的。然后，我们基于这些结构设计了事件关系学习。我们将这种学习封装到指令微调框架中，以更好地激发我们模型的事件推理能力。我们设计了一种启发式无监督方法，从大规模语料库中挖掘事件四元组。最后，我们在我们的事件导向指令微调上微调了一个Llama模型。我们在多个数据集上对事件推理任务进行了广泛实验。自动评估和人工评估表明，EvIT在事件推理上取得了有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
