@inproceedings{zgreaban-suresh-2023-prompting,
title = "Prompting {C}hat{GPT} to Draw Morphological Connections for New Word Comprehension",
author = "Zgreaban, Bianca-Madalina and
Suresh, Rishabh",
editor = "Hardalov, Momchil and
Kancheva, Zara and
Velichkov, Boris and
Nikolova-Koleva, Ivelina and
Slavcheva, Milena",
booktitle = "Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-stud.11",
pages = "98--107",
abstract = "Though more powerful, Large Language Models need to be periodically retrained for updated information, consuming resources and energy. In this respect, prompt engineering can prove a possible solution to re-training. To explore this line of research, this paper uses a case study, namely, finding the best prompting strategy for asking ChatGPT to define new words based on morphological connections. To determine the best prompting strategy, each definition provided by the prompt was ranked in terms of plausibility and humanlikeness criteria. The findings of this paper show that adding contextual information, operationalised as the keywords {`}new{'} and {`}morpheme{'}, significantly improve the performance of the model for any prompt. While no single prompt significantly outperformed all others, there were differences between performances on the two criteria for most prompts. ChatGPT also provided the most correct definitions with a persona-type prompt.",
}
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<abstract>Though more powerful, Large Language Models need to be periodically retrained for updated information, consuming resources and energy. In this respect, prompt engineering can prove a possible solution to re-training. To explore this line of research, this paper uses a case study, namely, finding the best prompting strategy for asking ChatGPT to define new words based on morphological connections. To determine the best prompting strategy, each definition provided by the prompt was ranked in terms of plausibility and humanlikeness criteria. The findings of this paper show that adding contextual information, operationalised as the keywords ‘new’ and ‘morpheme’, significantly improve the performance of the model for any prompt. While no single prompt significantly outperformed all others, there were differences between performances on the two criteria for most prompts. ChatGPT also provided the most correct definitions with a persona-type prompt.</abstract>
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%0 Conference Proceedings
%T Prompting ChatGPT to Draw Morphological Connections for New Word Comprehension
%A Zgreaban, Bianca-Madalina
%A Suresh, Rishabh
%Y Hardalov, Momchil
%Y Kancheva, Zara
%Y Velichkov, Boris
%Y Nikolova-Koleva, Ivelina
%Y Slavcheva, Milena
%S Proceedings of the 8th Student Research Workshop associated with the International Conference Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F zgreaban-suresh-2023-prompting
%X Though more powerful, Large Language Models need to be periodically retrained for updated information, consuming resources and energy. In this respect, prompt engineering can prove a possible solution to re-training. To explore this line of research, this paper uses a case study, namely, finding the best prompting strategy for asking ChatGPT to define new words based on morphological connections. To determine the best prompting strategy, each definition provided by the prompt was ranked in terms of plausibility and humanlikeness criteria. The findings of this paper show that adding contextual information, operationalised as the keywords ‘new’ and ‘morpheme’, significantly improve the performance of the model for any prompt. While no single prompt significantly outperformed all others, there were differences between performances on the two criteria for most prompts. ChatGPT also provided the most correct definitions with a persona-type prompt.
%U https://aclanthology.org/2023.ranlp-stud.11
%P 98-107
Markdown (Informal)
[Prompting ChatGPT to Draw Morphological Connections for New Word Comprehension](https://aclanthology.org/2023.ranlp-stud.11) (Zgreaban & Suresh, RANLP 2023)
ACL