@inproceedings{wachowiak-gromann-2023-gpt,
title = "Does {GPT}-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models",
author = "Wachowiak, Lennart and
Gromann, Dagmar",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.58",
doi = "10.18653/v1/2023.acl-long.58",
pages = "1018--1032",
abstract = "Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal. We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated. To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor{'}s source domain without any pre-set domains. We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets. When provided 12 few-shot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15{\%} in English and 34.65{\%} in Spanish. GPT{'}s most common error is a hallucinated source domain for which no indicator is present in the sentence. Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain.",
}
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%0 Conference Proceedings
%T Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models
%A Wachowiak, Lennart
%A Gromann, Dagmar
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wachowiak-gromann-2023-gpt
%X Conceptual metaphors present a powerful cognitive vehicle to transfer knowledge structures from a source to a target domain. Prior neural approaches focus on detecting whether natural language sequences are metaphoric or literal. We believe that to truly probe metaphoric knowledge in pre-trained language models, their capability to detect this transfer should be investigated. To this end, this paper proposes to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor’s source domain without any pre-set domains. We experiment with different training sample configurations for fine-tuning and few-shot prompting on two distinct datasets. When provided 12 few-shot samples in the prompt, GPT-3 generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. GPT’s most common error is a hallucinated source domain for which no indicator is present in the sentence. Other common errors include identifying a sequence as literal even though a metaphor is present and predicting the wrong source domain based on specific words in the sequence that are not metaphorically related to the target domain.
%R 10.18653/v1/2023.acl-long.58
%U https://aclanthology.org/2023.acl-long.58
%U https://doi.org/10.18653/v1/2023.acl-long.58
%P 1018-1032
Markdown (Informal)
[Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models](https://aclanthology.org/2023.acl-long.58) (Wachowiak & Gromann, ACL 2023)
ACL