@inproceedings{jiao-etal-2023-instruct,
title = "Instruct and Extract: Instruction Tuning for On-Demand Information Extraction",
author = "Jiao, Yizhu and
Zhong, Ming and
Li, Sha and
Zhao, Ruining and
Ouyang, Siru and
Ji, Heng and
Han, Jiawei",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.620",
doi = "10.18653/v1/2023.emnlp-main.620",
pages = "10030--10051",
abstract = "Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction {--} a classic task in natural language processing {--} most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, termed On-Demand Information Extraction, to fulfill the personalized demands of real-world users. Our task aims to follow the instructions to extract the desired content from the associated text and present it in a structured tabular format. The table headers can either be user-specified or inferred contextually by the model. To facilitate research in this emerging area, we present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE. Comprehensive evaluations on our benchmark reveal that ODIE substantially outperforms the existing open-source models of similar size.",
}
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<abstract>Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction – a classic task in natural language processing – most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, termed On-Demand Information Extraction, to fulfill the personalized demands of real-world users. Our task aims to follow the instructions to extract the desired content from the associated text and present it in a structured tabular format. The table headers can either be user-specified or inferred contextually by the model. To facilitate research in this emerging area, we present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE. Comprehensive evaluations on our benchmark reveal that ODIE substantially outperforms the existing open-source models of similar size.</abstract>
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%0 Conference Proceedings
%T Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
%A Jiao, Yizhu
%A Zhong, Ming
%A Li, Sha
%A Zhao, Ruining
%A Ouyang, Siru
%A Ji, Heng
%A Han, Jiawei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jiao-etal-2023-instruct
%X Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction – a classic task in natural language processing – most task-specific systems cannot align well with long-tail ad hoc extraction use cases for non-expert users. To address this, we propose a novel paradigm, termed On-Demand Information Extraction, to fulfill the personalized demands of real-world users. Our task aims to follow the instructions to extract the desired content from the associated text and present it in a structured tabular format. The table headers can either be user-specified or inferred contextually by the model. To facilitate research in this emerging area, we present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE. Comprehensive evaluations on our benchmark reveal that ODIE substantially outperforms the existing open-source models of similar size.
%R 10.18653/v1/2023.emnlp-main.620
%U https://aclanthology.org/2023.emnlp-main.620
%U https://doi.org/10.18653/v1/2023.emnlp-main.620
%P 10030-10051
Markdown (Informal)
[Instruct and Extract: Instruction Tuning for On-Demand Information Extraction](https://aclanthology.org/2023.emnlp-main.620) (Jiao et al., EMNLP 2023)
ACL