The Mutual Reinforcement Effect (MRE) represents a promising avenue in information extraction and multitasking research. Nevertheless, its applicability has been constrained due to the exclusive availability of MRE mix datasets in Japanese, thereby limiting comprehensive exploration by the global research community. To address this limitation, we introduce a Multilingual MRE mix dataset (MMM) that encompasses 21 sub-datasets in English, Japanese, and Chinese. In this paper, we also propose a method for dataset translation assisted by Large Language Models (LLMs), which significantly reduces the manual annotation time required for dataset construction by leveraging LLMs to translate the original Japanese datasets. Additionally, we have enriched the dataset by incorporating open-domain Named Entity Recognition (NER) and sentence classification tasks. Utilizing this expanded dataset, we developed a unified input-output framework to train an Open-domain Information Extraction Large Language Model (OIELLM). The OIELLM model demonstrates the capability to effectively process novel MMM datasets, exhibiting significant improvements in performance.

翻译：互增强效应（MRE）在信息抽取和多任务研究领域展现出广阔前景。然而，由于现有的MRE混合数据集仅限日语版本，其应用范围受到限制，阻碍了全球研究界的深入探索。为突破此局限，我们提出了一个涵盖英语、日语和中文共21个子数据集的多语言MRE混合数据集（MMM）。本文同时提出一种基于大语言模型（LLMs）辅助的数据集翻译方法，通过利用LLMs翻译原始日语数据集，显著减少了数据集构建所需的人工标注时间。此外，我们通过引入开放域命名实体识别（NER）和句子分类任务对数据集进行了扩充。基于该扩展数据集，我们开发了统一的输入-输出框架以训练开放域信息抽取大语言模型（OIELLM）。OIELLM模型展现出处理新型MMM数据集的有效能力，其性能表现获得显著提升。