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arXiv · 2306.01183

Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation

Adithya V Ganesan·Yash Kumar Lal·August Håkan Nilsson·H. Andrew Schwartz
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Abstract

Very large language models (LLMs) perform extremely well on a spectrum of NLP tasks in a zero-shot setting. However, little is known about their performance on human-level NLP problems which rely on understanding psychological concepts, such as assessing personality traits. In this work, we investigate the zero-shot ability of GPT-3 to estimate the Big 5 personality traits from users' social media posts. Through a set of systematic experiments, we find that zero-shot GPT-3 performance is somewhat close to an existing pre-trained SotA for broad classification upon injecting knowledge about the trait in the prompts. However, when prompted to provide fine-grained classification, its performance drops to close to a simple most frequent class (MFC) baseline. We further analyze where GPT-3 performs better, as well as worse, than a pretrained lexical model, illustrating systematic errors that suggest ways to improve LLMs on human-level NLP tasks.

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Adithya V Ganesan, Yash Kumar Lal, August Håkan Nilsson, H. Andrew Schwartz. 2023-06-01. Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation. https://arxiv.org/abs/2306.01183

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