Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/36230
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Silva, Kanishka
Frommholz, Ingo
Can, Burcu
Blain, Frédéric
Sarwar, Raheem
Ugolini, Laura
Contact Email: burcu.can@stir.ac.uk
Title: Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels
Citation: Silva K, Frommholz I, Can B, Blain F, Sarwar R & Ugolini L (2024) Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels. In: <i>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop</i>, 21.03.2024-22.03.2024. ACL, pp. 325-337. https://aclanthology.org/2024.eacl-srw.26.pdf
Issue Date: 20-Mar-2024
Date Deposited: 30-Jul-2024
Series/Report no.: 2024.eacl-srw.26
Conference Dates: 2024-03-21 - 2024-03-22
Abstract: The advancement of generative Large Language Models (LLMs), capable of producing human-like texts, introduces challenges related to the authenticity of the text documents. This requires exploring potential forgery scenarios within the context of authorship attribution, especially in the literary domain. Particularly, two aspects of doubted authorship may arise in novels, as a novel may be imposed by a renowned author or include a copied writing style of a well-known novel. To address these concerns, we introduce Forged-GAN-BERT, a modified GAN-BERT-based model to improve the classification of forged novels in two data-augmentation aspects: via the Forged Novels Generator (i.e., ChatGPT) and the generator in GAN. Compared to other transformer-based models, the proposed Forged-GAN-BERT model demonstrates an improved performance with F1 scores of 0.97 and 0.71 for identifying forged novels in single-author and multi-author classification settings. Additionally, we explore different prompt categories for generating the forged novels to analyse the quality of the generated texts using different similarity distance measures , including ROUGE-1, Jaccard Similarity, Overlap Confident, and Cosine Similarity.
Status: VoR - Version of Record
Rights: Copyright © 2024 Association for Computational Linguistics. Licensed on a Creative Commons Attribution 4.0 International License.
URL: https://aclanthology.org/2024.eacl-srw.26.pdf
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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