ItemConference Paper

Can LLMs Reliably Predict Personality Traits Through Prompt Engineering? Introducing Expert-Chain-Hypothesis Prompting

Title
Can LLMs Reliably Predict Personality Traits Through Prompt Engineering? Introducing Expert-Chain-Hypothesis Prompting
Creator
John, Juswin Sajan; George, Shiju; Mukhopadhyay, Debarka
Description
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.
Source
Lecture Notes in Networks and Systems;Volume;1929 LNNS;pp.86-98
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Big Five; Expert-Chain-Hypothesis; LLMs; Personality Traits; Prompt Engineering
Coverage
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
Rights
All Open Access; Hybrid Gold Open Access
Relation
ISSN: 23673370; ISBN: 978-303222910-6;
Format
online
Language
English
Type
Conference paper

Citation

John, Juswin Sajan; George, Shiju; Mukhopadhyay, Debarka, “Can LLMs Reliably Predict Personality Traits Through Prompt Engineering? Introducing Expert-Chain-Hypothesis Prompting,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25415.

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