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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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GPT is an effective tool for multilingual psychological text analysis.

Steve Rathje¹, Dan-Mircea Mirea², Ilia Sucholutsky³

¹Department of Psychology, New York University, New York, NY 10003.

Proceedings of the National Academy of Sciences of the United States of America

|August 12, 2024

Summary

Large-language models like GPT offer a powerful, accessible tool for automated psychological text analysis across multiple languages. These models demonstrate superior accuracy compared to traditional methods, democratizing advanced natural language processing for research.

Keywords:

AI GPT large language models machine learning text analysis

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Area of Science:

Social and behavioral sciences
Computational linguistics
Psychological science

Background:

Automated text analysis is increasingly used in social and behavioral sciences to measure psychological constructs.
Existing methods often lack cross-linguistic capabilities and require significant expertise.

Purpose of the Study:

To evaluate the efficacy of Generative Pre-trained Transformer (GPT) models for automated psychological text analysis in multiple languages.
To compare GPT's performance against traditional dictionary-based methods and fine-tuned machine learning models.

Main Methods:

Utilized 15 datasets comprising 47,925 annotated tweets and news headlines.
Tested GPT versions (3.5 Turbo, 4, 4 Turbo) for detecting psychological constructs (sentiment, emotions, offensiveness, moral foundations) across 12 languages.
Compared GPT performance with English-language dictionary analysis and established machine learning models.

Main Results:

GPT models (r = 0.59 to 0.77) significantly outperformed English-language dictionary analysis (r = 0.20 to 0.30).
GPT performance was comparable to, and sometimes exceeded, top-performing fine-tuned machine learning models.
Performance improved with newer GPT versions, especially for less common languages, and costs decreased.

Conclusions:

GPT models provide a highly accurate, versatile, and user-friendly alternative for automated psychological text analysis.
GPT's ability to function without training data and with simple prompts democratizes advanced NLP for researchers, including those studying under-resourced languages.
LLMs facilitate cross-linguistic research and make sophisticated text analysis more accessible to a broader scientific community.