@inproceedings{sun-etal-2024-chinese,
title = "{C}hinese {UMR} annotation: Can {LLM}s help?",
author = "Sun, Haibo and
Xue, Nianwen and
Zhao, Jin and
Yue, Liulu and
Sun, Yao and
Xu, Keer and
Wu, Jiawei",
editor = "Bonial, Claire and
Bonn, Julia and
Hwang, Jena D.",
booktitle = "Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.dmr-1.14",
pages = "131--139",
abstract = "We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks.",
}
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<abstract>We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks.</abstract>
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%0 Conference Proceedings
%T Chinese UMR annotation: Can LLMs help?
%A Sun, Haibo
%A Xue, Nianwen
%A Zhao, Jin
%A Yue, Liulu
%A Sun, Yao
%A Xu, Keer
%A Wu, Jiawei
%Y Bonial, Claire
%Y Bonn, Julia
%Y Hwang, Jena D.
%S Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F sun-etal-2024-chinese
%X We explore using LLMs, GPT-4 specifically, to generate draft sentence-level Chinese Uniform Meaning Representations (UMRs) that human annotators can revise to speed up the UMR annotation process. In this study, we use few-shot learning and Think-Aloud prompting to guide GPT-4 to generate sentence-level graphs of UMR. Our experimental results show that compared with annotating UMRs from scratch, using LLMs as a preprocessing step reduces the annotation time by two thirds on average. This indicates that there is great potential for integrating LLMs into the pipeline for complicated semantic annotation tasks.
%U https://aclanthology.org/2024.dmr-1.14
%P 131-139
Markdown (Informal)
[Chinese UMR annotation: Can LLMs help?](https://aclanthology.org/2024.dmr-1.14) (Sun et al., DMR-WS 2024)
ACL
- Haibo Sun, Nianwen Xue, Jin Zhao, Liulu Yue, Yao Sun, Keer Xu, and Jiawei Wu. 2024. Chinese UMR annotation: Can LLMs help?. In Proceedings of the Fifth International Workshop on Designing Meaning Representations @ LREC-COLING 2024, pages 131–139, Torino, Italia. ELRA and ICCL.