@inproceedings{kim-etal-2023-chatgpt,
title = "Can {C}hat{GPT} Understand Causal Language in Science Claims?",
author = "Kim, Yuheun and
Guo, Lu and
Yu, Bei and
Li, Yingya",
editor = "Barnes, Jeremy and
De Clercq, Orph{\'e}e and
Klinger, Roman",
booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.wassa-1.33",
doi = "10.18653/v1/2023.wassa-1.33",
pages = "379--389",
abstract = "This study evaluated ChatGPT{'}s ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship. The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin. ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges. However, its weakness may be utilized to improve the clarity of human annotation guideline. Chain-of-Thoughts were faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.",
}
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<abstract>This study evaluated ChatGPT’s ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship. The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin. ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges. However, its weakness may be utilized to improve the clarity of human annotation guideline. Chain-of-Thoughts were faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.</abstract>
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%0 Conference Proceedings
%T Can ChatGPT Understand Causal Language in Science Claims?
%A Kim, Yuheun
%A Guo, Lu
%A Yu, Bei
%A Li, Yingya
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kim-etal-2023-chatgpt
%X This study evaluated ChatGPT’s ability to understand causal language in science papers and news by testing its accuracy in a task of labeling the strength of a claim as causal, conditional causal, correlational, or no relationship. The results show that ChatGPT is still behind the existing fine-tuned BERT models by a large margin. ChatGPT also had difficulty understanding conditional causal claims mitigated by hedges. However, its weakness may be utilized to improve the clarity of human annotation guideline. Chain-of-Thoughts were faithful and helpful for improving prompt performance, but finding the optimal prompt is difficult with inconsistent results and the lack of effective method to establish cause-effect between prompts and outcomes, suggesting caution when generalizing prompt engineering results across tasks or models.
%R 10.18653/v1/2023.wassa-1.33
%U https://aclanthology.org/2023.wassa-1.33
%U https://doi.org/10.18653/v1/2023.wassa-1.33
%P 379-389
Markdown (Informal)
[Can ChatGPT Understand Causal Language in Science Claims?](https://aclanthology.org/2023.wassa-1.33) (Kim et al., WASSA 2023)
ACL
- Yuheun Kim, Lu Guo, Bei Yu, and Yingya Li. 2023. Can ChatGPT Understand Causal Language in Science Claims?. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 379–389, Toronto, Canada. Association for Computational Linguistics.