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