@inproceedings{dinu-florescu-2024-comparing,
title = "Comparing Large Language Models Verbal Creativity to Human Verbal Creativity",
author = "Dinu, Anca and
Florescu, Andra",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.37/",
pages = "308--315",
ISBN = "979-12-210-7060-6",
abstract = "This study investigates verbal creativity differences and similarities between Large Language Models and humans, based ontheir answers given to the integrated verbal creativity test in [1 ]. Since this article reported a very small difference of scoresin favour of the machines, the aim of the present work is to thoroughly analyse the data through four methods: scoring theuniqueness of the answers of one human or one machine compared to all the others, semantic similarity clustering, binaryclassification and manual inspection of the data. The results showed that humans and machines are on a par in terms ofuniqueness scores, that humans and machines group in two well defined clusters based on semantics similarities, and that theanswers are not so easy to automatically classify in human answers and LLM answers."
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<abstract>This study investigates verbal creativity differences and similarities between Large Language Models and humans, based ontheir answers given to the integrated verbal creativity test in [1 ]. Since this article reported a very small difference of scoresin favour of the machines, the aim of the present work is to thoroughly analyse the data through four methods: scoring theuniqueness of the answers of one human or one machine compared to all the others, semantic similarity clustering, binaryclassification and manual inspection of the data. The results showed that humans and machines are on a par in terms ofuniqueness scores, that humans and machines group in two well defined clusters based on semantics similarities, and that theanswers are not so easy to automatically classify in human answers and LLM answers.</abstract>
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%0 Conference Proceedings
%T Comparing Large Language Models Verbal Creativity to Human Verbal Creativity
%A Dinu, Anca
%A Florescu, Andra
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F dinu-florescu-2024-comparing
%X This study investigates verbal creativity differences and similarities between Large Language Models and humans, based ontheir answers given to the integrated verbal creativity test in [1 ]. Since this article reported a very small difference of scoresin favour of the machines, the aim of the present work is to thoroughly analyse the data through four methods: scoring theuniqueness of the answers of one human or one machine compared to all the others, semantic similarity clustering, binaryclassification and manual inspection of the data. The results showed that humans and machines are on a par in terms ofuniqueness scores, that humans and machines group in two well defined clusters based on semantics similarities, and that theanswers are not so easy to automatically classify in human answers and LLM answers.
%U https://aclanthology.org/2024.clicit-1.37/
%P 308-315
Markdown (Informal)
[Comparing Large Language Models Verbal Creativity to Human Verbal Creativity](https://aclanthology.org/2024.clicit-1.37/) (Dinu & Florescu, CLiC-it 2024)
ACL