Harnessing Large Language Models for Effective and Efficient Hate Speech Detection

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dc.contributor.authorSvetasheva, Arina
dc.contributor.authorLee, Keeheon
dc.date.accessioned2023-12-26T18:51:42Z
dc.date.available2023-12-26T18:51:42Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.826
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other58600897-e631-4405-af75-99a20b121906
dc.identifier.urihttps://hdl.handle.net/10125/107212
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial Intelligence and Digital Discrimination
dc.subjecthate speech detection — large language models — synthetic datasets — online toxicity
dc.titleHarnessing Large Language Models for Effective and Efficient Hate Speech Detection
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractHate speech presents a growing concern within online communities, posing threats to marginalized groups and undermining ethical norms. Although automatic hate speech detection (AHSD) methods have shown promise, there is still room for improvement. Recent advancements in Language Model Pretraining, exemplified by the introduction of ChatGPT-4, bring forth new possibilities for enhancing classification. In this study, we propose leveraging synthetic data generation to improve hate speech detection. Our findings demonstrate the effectiveness and efficiency of this approach in rapidly improving model performance, particularly in scenarios where obtaining sufficient amounts of hate speech data is challenging. Through our experiments, we establish that Large Language Models (LLMs) can proficiently serve as both data generators and annotators in the desired format, exhibiting performance comparable to, and even surpassing, that of humans. Moreover, we validate the applicability of LLMs in domains characterized by complex and highly abbreviated lexicons, such as the gaming industry.
dcterms.extent10 pages
prism.startingpage6898

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