fm_data_tasks

该数据集是一系列用于评估大型语言模型（LLMs）在不同数据处理任务中性能的集合，这些任务包括错误检测、数据填充、模式匹配和实体匹配。这些数据集已在先前的研究中被广泛使用，其规模从65个实例到17101个实例不等，平均数量约为3680个，涉及的任务包括错误检测、数据填充、模式匹配和实体匹配。

This collection of datasets is a suite of benchmarks designed to evaluate the performance of Large Language Models (LLMs) across various data processing tasks, including error detection, data filling, pattern matching, and entity matching. These datasets have been widely adopted in prior scholarly research, with their instance counts ranging from 65 to 17101, averaging approximately 3680 instances, and they cover the aforementioned data processing tasks.