LLMs in the Generation of Seismic Alert Communiqués

Oscar Peña-Cáceres; Henry Silva-Marchan; Rudy Espinoza-Nima; Manuel More-More; Mariela  Chauca-Claros; Jorge Yánez-Palacios

doi:10.48161/qaj.v5n2a1400

Authors

Oscar Peña-Cáceres Professional School of Systems Engineering, Universidad César Vallejo, Piura 20001, Peru; Superior Technical School of Engineering, Departament of Informatics, Universitat de València, Burjassot Valencia 46100, Spain;
Henry Silva-Marchan Department of Mathematics, Statistics and Informatics, Universidad Nacional de Tumbes, Tumbes 24000, Peru;
Rudy Espinoza-Nima Department of Physics, Faculty of Science, Universidad Nacional de Piura, Piura 20001, Peru;
Manuel More-More Department of Physics, Faculty of Science, Universidad Nacional de Piura, Piura 20001, Peru;
Mariela Chauca-Claros Department of Language Theory and Communication Sciences, Universitat de València, Burjassot Valencia 46100, Spain;
Jorge Yánez-Palacios Faculty of Philosophy, Letters and Educational Sciences, Universidad de Guayaquil, Guayaquil 090514, Ecuador.

DOI:

https://doi.org/10.48161/qaj.v5n2a1400

Keywords:

LLMs, ChatGPT, earthquake, alert, disasters, Peru.

Abstract

Peru is in the Ring of Fire, a zone of high seismic activity. Currently, alerts generated by technical-scientific entities are often bland and lack precise geographic context, resulting in alerts of limited usefulness for informing the public. In this paper, we present a conceptual model and architecture to explore the potential of Large Language Models (LLMs) to produce various forms of seismic warnings tailored to the particularities that would be required for different geographic areas of a given locality in Peru. The proposal was evaluated in a controlled environment with the participation of 47 users with diverse ethnographic characteristics. The context of the study was explained to them, and they were provided with a questionnaire designed to assess the ease of understanding, usefulness and quality of the content of the alert communications generated by an LLM. The results show that, according to the indicators assessed, seismic warnings generated by an LLM are 76% easy to understand, 81% useful and 71% acceptable quality.

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LLMs in the Generation of Seismic Alert Communiqués

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