Key elements of human events are extracted as quadruples that consist of
subject, relation, object, and timestamp. This representation can be extended
to a quintuple by adding a fifth element: a textual summary that briefly
describes the event. These quadruples or quintuples, when organized within a
specific domain, form a temporal knowledge graph (TKG). Current learning
frameworks focus on a few TKG-related tasks, such as predicting an object given
a subject and a relation or forecasting the occurrences of multiple types of
events (i.e., relation) in the next time window. They typically rely on complex
structural and sequential models like graph neural networks (GNNs) and
recurrent neural networks (RNNs) to update intermediate embeddings. However,
these methods often neglect the contextual information inherent in each
quintuple, which can be effectively captured through concise textual
descriptions. In this paper, we investigate how large language models (LLMs)
can streamline the design of TKG learning frameworks while maintaining
competitive accuracy in prediction and forecasting tasks. We develop multiple
prompt templates to frame the object prediction (OP) task as a standard
question-answering (QA) task, suitable for instruction fine-tuning with an
encoder-decoder generative LLM. For multi-event forecasting (MEF), we design
simple yet effective prompt templates for each TKG quintuple. This novel
approach removes the need for GNNs and RNNs, instead utilizing an encoder-only
LLM to generate fixed intermediate embeddings, which are subsequently processed
by a prediction head with a self-attention mechanism to forecast potential
future relations. Extensive experiments on multiple real-world datasets using
various evaluation metrics validate the effectiveness and robustness of our
approach.

通过使用大型语言模型，本文研究了如何在维持竞争性准确率的同时，简化时态知识图谱学习框架的设计，通过使用指令微调适合编码器 - 解码器生成式语言模型进行对象预测，并设计了简单而有效的提示模板，用于每个 TKG 五元组的多事件预测。实验结果表明，此方法具有有效性和鲁棒性。

大型语言模型作为事件预测器

Large Language Models as Event Forecasters

This paper presents a comprehensive overview of the Comparative Opinion
Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the
10$^{th}$ International Workshop on Vietnamese Language and Speech Processing
(VLSP 2023). The primary objective of this shared task is to advance the field
of natural language processing by developing techniques that proficiently
extract comparative opinions from Vietnamese product reviews. Participants are
challenged to propose models that adeptly extract a comparative "quintuple"
from a comparative sentence, encompassing Subject, Object, Aspect, Predicate,
and Comparison Type Label. We construct a human-annotated dataset comprising
$120$ documents, encompassing $7427$ non-comparative sentences and $2468$
comparisons within $1798$ sentences. Participating models undergo evaluation
and ranking based on the Exact match macro-averaged quintuple F1 score.

本研究综述了越南产品评论比较观点挖掘的比较任务，该任务旨在通过开发从越南产品评论中有效提取比较观点的技术，推进自然语言处理领域。研究构建了一个人工标注数据集，包括 120 个文档、7427 个非比较句和 1798 个句子中的 2468 个比较，参与模型根据完全匹配宏平均的五元组 F1 得分进行评估和排名。