title: MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with
    Open-domain Information Extraction Large Language Models
summary: 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.
paper_authors._id: 6695fdc00e08a6505b828cdd
paper_authors.name: Chengguang Gan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-18T09:29:36.357Z
paper_authors._id: 629311a945f405d06678224b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1661878264498-629311a945f405d06678224b.png
paper_authors.fullname: Chengguang Gan
paper_authors.type: user
paper_authors.user: ganchengguang
paper_authors._id: 6695fdc00e08a6505b828cde
paper_authors.name: Qingyu Yin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-22T19:07:43.039Z
paper_authors._id: 6453cb22908e259483c0a061
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6453cb22908e259483c0a061/hMgdwZUsUbgquGalzPGzV.jpeg
paper_authors.fullname: Qingyu_Yin
paper_authors.type: user
paper_authors.user: MikaStars39
paper_authors._id: 6695fdc00e08a6505b828cdf
paper_authors.name: Xinyang He
paper_authors._id: 6695fdc00e08a6505b828ce0
paper_authors.name: Hanjun Wei
paper_authors._id: 6695fdc00e08a6505b828ce1
paper_authors.name: Yunhao Liang
paper_authors._id: 6695fdc00e08a6505b828ce2
paper_authors.name: Younghun Lim
paper_authors._id: 6695fdc00e08a6505b828ce3
paper_authors.name: Shijian Wang
paper_authors._id: 6695fdc00e08a6505b828ce4
paper_authors.name: Hexiang Huang
paper_authors._id: 6695fdc00e08a6505b828ce5
paper_authors.name: Qinghao Zhang
paper_authors._id: 6695fdc00e08a6505b828ce6
paper_authors.name: Shiwen Ni
paper_authors._id: 6695fdc00e08a6505b828ce7
paper_authors.name: Tatsunori Mori
paper_title: MMM: Multilingual Mutual Reinforcement Effect Mix Datasets & Test with
    Open-domain Information Extraction Large Language Models
paper_summary: 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.
paper_ai_summary: A multilingual dataset and a unified framework for information extraction using large language models are introduced, enhancing performance and accessibility across different languages.
paper_ai_keywords: Mutual Reinforcement Effect Multilingual MRE mix dataset Large Language Models dataset translation Named Entity Recognition sentence classification Open-domain Information Extraction Large Language Model unified input-output framework