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Evaluating coherence in AI-generated text

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Evaluating coherence in AI-generated text

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dc.contributor.author Olmedilla, María es_ES
dc.contributor.author Romero, José Carlos es_ES
dc.contributor.author Martínez-Torres, Rocío es_ES
dc.contributor.author Toral, Sergio es_ES
dc.contributor.author Galvan, Nicolas R. es_ES
dc.date.accessioned 2024-09-17T11:50:32Z
dc.date.available 2024-09-17T11:50:32Z
dc.date.issued 2024-07-16
dc.identifier.isbn 9788413962016
dc.identifier.uri http://hdl.handle.net/10251/208240
dc.description.abstract [EN] This study examines the role of coherence in AI-generated online reviews and its effect on perceived authenticity and consumer trust. By applying advanced metrics like BERT Score, BART Score, and Disco Score, the research analyzes the coherence of AI-generated text using Generative AI models, specifically Llama-2, on Amazon beauty product reviews. Results indicate that AI-generated reviews exhibit higher coherence compared to human-generated content, suggesting that Generative AI can produce seemingly authentic content. This finding challenges the ability to distinguish between human and AI-generated reviews, raising important questions about consumer trust in digital marketplaces. The study underscores the importance of coherence in online content's credibility and opens avenues for further research on Generative AI's role in e-commerce. es_ES
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 6th International Conference on Advanced Research Methods and Analytics (CARMA 2024)
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Generative-AI es_ES
dc.subject Online reviews es_ES
dc.subject Llama-2 es_ES
dc.subject BERT es_ES
dc.subject Coherence es_ES
dc.title Evaluating coherence in AI-generated text es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/CARMA2024.2024.17820
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Olmedilla, M.; Romero, JC.; Martínez-Torres, R.; Toral, S.; Galvan, NR. (2024). Evaluating coherence in AI-generated text. Editorial Universitat Politècnica de València. 149-156. https://doi.org/10.4995/CARMA2024.2024.17820 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename CARMA 2024 - 6th International Conference on Advanced Research Methods and Analytics es_ES
dc.relation.conferencedate Junio 26-28, 2024 es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/CARMA/CARMA2024/paper/view/17820 es_ES
dc.description.upvformatpinicio 149 es_ES
dc.description.upvformatpfin 156 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela OCS\17820 es_ES


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