@inproceedings{wu-etal-2024-perplexing,
title = "Perplexing Canon: A study on {GPT}-based perplexity of canonical and non-canonical literary works",
author = "Wu, Yaru and
Bizzoni, Yuri and
Moreira, Pascale and
Nielbo, Kristoffer",
editor = "Bizzoni, Yuri and
Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Szpakowicz, Stan",
booktitle = "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.latechclfl-1.16",
pages = "172--184",
abstract = "This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect {``}strategies{''} to achieve an informationally dense literary style.",
}
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<abstract>This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect “strategies” to achieve an informationally dense literary style.</abstract>
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%0 Conference Proceedings
%T Perplexing Canon: A study on GPT-based perplexity of canonical and non-canonical literary works
%A Wu, Yaru
%A Bizzoni, Yuri
%A Moreira, Pascale
%A Nielbo, Kristoffer
%Y Bizzoni, Yuri
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Szpakowicz, Stan
%S Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F wu-etal-2024-perplexing
%X This study extends previous research on literary quality by using information theory-based methods to assess the level of perplexity recorded by three large language models when processing 20th-century English novels deemed to have high literary quality, recognized by experts as canonical, compared to a broader control group. We find that canonical texts appear to elicit a higher perplexity in the models, we explore which textual features might concur to create such an effect. We find that the usage of a more heavily nominal style, together with a more diverse vocabulary, is one of the leading causes of the difference between the two groups. These traits could reflect “strategies” to achieve an informationally dense literary style.
%U https://aclanthology.org/2024.latechclfl-1.16
%P 172-184
Markdown (Informal)
[Perplexing Canon: A study on GPT-based perplexity of canonical and non-canonical literary works](https://aclanthology.org/2024.latechclfl-1.16) (Wu et al., LaTeCHCLfL-WS 2024)
ACL