@inproceedings{song-etal-2024-itake,
title = "{ITAKE}: Interactive Unstructured Text Annotation and Knowledge Extraction System with {LLM}s and {M}odel{O}ps",
author = "Song, Jiahe and
Ding, Hongxin and
Wang, Zhiyuan and
Xu, Yongxin and
Wang, Yasha and
Zhao, Junfeng",
editor = "Cao, Yixin and
Feng, Yang and
Xiong, Deyi",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-demos.31",
pages = "326--334",
abstract = "Extracting structured knowledge from unstructured text data has a wide range of application prospects, and a pervasive trend is to develop text annotation tools to help extraction. However, they often encounter issues such as single scenario usage, lack of effective human-machine collaboration, insufficient model supervision, and suboptimal utilization of Large Language Models (LLMs). We introduces an interactive unstructured text annotation and knowledge extraction system that synergistically integrates LLMs and ModelOps to alleviate these issues. The system leverages LLMs for enhanced performance in low-resource contexts, employs a ModelOps platform to monitor models throughout their lifecycle, and amalgamates interactive annotation methods with online machine learning and active learning. The demo video and website are now publicly available.",
}
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<abstract>Extracting structured knowledge from unstructured text data has a wide range of application prospects, and a pervasive trend is to develop text annotation tools to help extraction. However, they often encounter issues such as single scenario usage, lack of effective human-machine collaboration, insufficient model supervision, and suboptimal utilization of Large Language Models (LLMs). We introduces an interactive unstructured text annotation and knowledge extraction system that synergistically integrates LLMs and ModelOps to alleviate these issues. The system leverages LLMs for enhanced performance in low-resource contexts, employs a ModelOps platform to monitor models throughout their lifecycle, and amalgamates interactive annotation methods with online machine learning and active learning. The demo video and website are now publicly available.</abstract>
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%0 Conference Proceedings
%T ITAKE: Interactive Unstructured Text Annotation and Knowledge Extraction System with LLMs and ModelOps
%A Song, Jiahe
%A Ding, Hongxin
%A Wang, Zhiyuan
%A Xu, Yongxin
%A Wang, Yasha
%A Zhao, Junfeng
%Y Cao, Yixin
%Y Feng, Yang
%Y Xiong, Deyi
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F song-etal-2024-itake
%X Extracting structured knowledge from unstructured text data has a wide range of application prospects, and a pervasive trend is to develop text annotation tools to help extraction. However, they often encounter issues such as single scenario usage, lack of effective human-machine collaboration, insufficient model supervision, and suboptimal utilization of Large Language Models (LLMs). We introduces an interactive unstructured text annotation and knowledge extraction system that synergistically integrates LLMs and ModelOps to alleviate these issues. The system leverages LLMs for enhanced performance in low-resource contexts, employs a ModelOps platform to monitor models throughout their lifecycle, and amalgamates interactive annotation methods with online machine learning and active learning. The demo video and website are now publicly available.
%U https://aclanthology.org/2024.acl-demos.31
%P 326-334
Markdown (Informal)
[ITAKE: Interactive Unstructured Text Annotation and Knowledge Extraction System with LLMs and ModelOps](https://aclanthology.org/2024.acl-demos.31) (Song et al., ACL 2024)
ACL