Natural language processing (NLP) has grown significantly since the advent of the Transformer architecture. Transformers have given birth to pre-trained large language models (PLMs). There has been tremendous improvement in the performance of NLP systems across several tasks. NLP systems are on par or, in some cases, better than humans at accomplishing specific tasks. However, it remains the norm that \emph{better quality datasets at the time of pretraining enable PLMs to achieve better performance, regardless of the task.} The need to have quality datasets has prompted NLP researchers to continue creating new datasets to satisfy particular needs. For example, the two top NLP conferences, ACL and EMNLP, accepted ninety-two papers in 2022, introducing new datasets. This work aims to uncover the trends and insights mined within these datasets. Moreover, we provide valuable suggestions to researchers interested in curating datasets in the future.

翻译：自Transformer架构问世以来，自然语言处理（NLP）领域取得了显著发展。Transformer催生了预训练大语言模型（PLMs），使得NLP系统在多项任务中的性能实现了巨大提升。在某些特定任务上，NLP系统已达到甚至超越了人类水平。然而，一个普遍规律依然是：\emph{在预训练阶段使用更高质量的数据集，能使PLMs在任何任务上都获得更优的性能。} 对高质量数据集的需求促使NLP研究者持续创建新数据集以满足特定需求。例如，ACL和EMNLP两大顶级NLP会议在2022年收录的论文中，有九十二篇引入了新的数据集。本研究旨在揭示这些数据集中蕴含的趋势与洞见。此外，我们为未来有意构建数据集的研究者提供了有价值的建议。