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ORIGINAL RESEARCH article

Front. Big Data
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1387325

Navigating Pathways to Automated Personality Prediction: A Comparative Study of Small and Medium Language Models

Provisionally accepted
Fatima Habib Fatima Habib 1*Zeeshan Ali Zeeshan Ali 2Akbar Azam Akbar Azam 1Komal Kamran Komal Kamran 1Fahad M. Pasha Fahad M. Pasha 3
  • 1 FAST School of Management, Islamabad, Pakistan
  • 2 Oxford Brookes University Business School, Oxford, England, United Kingdom
  • 3 Lahore School of Economics, Lahore, Punjab, Pakistan

The final, formatted version of the article will be published soon.

    Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift towards more computationally efficient smaller models is observed. This study compares a small model ALBERT (11.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting big five personality traits. It utilizes the PANDORA dataset comprising Reddit comments, processing them on a Tesla P100-PCIE-16GB GPU. The study customized both models to support multi-output regression and added two linear layers for fine-grained regression analysis. Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), considering the computational resources consumed during training. While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. The study produced comparable levels of MSE, RMSE, and training loss reduction. This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. Theoretical and practical implications are also discussed.

    Keywords: Automated personality prediction, Big Five personality model, Natural Language Processing, Social media text, Muti-output regression, Large language models

    Received: 05 Mar 2024; Accepted: 28 Aug 2024.

    Copyright: © 2024 Habib, Ali, Azam, Kamran and Pasha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Fatima Habib, FAST School of Management, Islamabad, Pakistan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.