Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource John, Juswin Sajan; George, Shiju; Mukhopadhyay, Debarka Title A name given to the resource Can LLMs Reliably Predict Personality Traits Through Prompt Engineering? Introducing Expert-Chain-Hypothesis Prompting Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.86-98 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-22911-3_7" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-22911-3_7</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039329567?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039329567?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant John J.S., Department of AI ans Data Science Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; George S., Department of AI ans Data Science Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Mukhopadhyay D., Department of AI ans Data Science Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India Description An account of the resource The prediction of personality traits from natural language is a long-standing challenge at the intersection of artificial intelligence and psychology. Traditional approaches have largely relied on handcrafted features, custom labelled datasets, supervised learning models or pretrained embeddings. However, relatively fewer studies have explored the potential of large language models (LLMs) through prompt engineering. This study investigates the capability of LLMs to reliably predict the Big Five personality traits from unstructured textual input using prompt-based methods. We introduce Expert-Chain-Hypothesis prompting, a psychology informed prompting technique that mirrors the decision making workflow of human experts within LLMs. Using the Essays dataset, we evaluate zero-shot prompting, few-shot prompting, chain-of-thought prompting and the proposed method on GPT - 4o. Experimental results demonstrate that the proposed prompting technique achieves superior overall performance; with a macro F1 score of 0.759, micro F1 score of 0.750 and the lowest reported hamming loss of 0.280. Our findings suggest that while prompt engineering techniques hold significant promise for personality prediction, challenges remain in fully capturing the subtleties of all personality traits. The Author(s) 2026. Subject The topic of the resource Big Five; Expert-Chain-Hypothesis; LLMs; Personality Traits; Prompt Engineering Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303222910-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online