Large language models can replicate cross-cultural differences in personality (2310.10679v2)

Abstract: We use a large-scale experiment (N=8000) to determine whether GPT-4 can replicate cross-cultural differences in the Big Five, measured using the Ten-Item Personality Inventory. We used the US and South Korea as the cultural pair, given that prior research suggests substantial personality differences between people from these two countries. We manipulated the target of the simulation (US vs. Korean), the language of the inventory (English vs. Korean), and the LLM (GPT-4 vs. GPT-3.5). Our results show that GPT-4 replicated the cross-cultural differences for each factor. However, mean ratings had an upward bias and exhibited lower variation than in the human samples, as well as lower structural validity. We provide preliminary evidence that LLMs can aid cross-cultural researchers and practitioners.

Analysis of "LLMs Can Replicate Cross-Cultural Differences in Personality"

The paper by Niszczota, Janczak, and Misiak examines the potential for LLMs to simulate cross-cultural personality differences using GPT-4 and GPT-3.5 models. It focuses on the Big Five personality traits evaluated through the Ten-Item Personality Inventory (TIPI) across two cultural contexts, the United States and South Korea. This paper provides a meticulous evaluation of the hypothesis that LLMs can replicate observed personality differences in a cross-cultural framework.

Methodology and Hypotheses

The research involves a robust experimental design using a 2x2x2 framework. The variables include the country of simulated origin (United States or South Korea), the language of the inventory (English or Korean), and the version of the LLM (GPT-4 or GPT-3.5). The paper includes a substantial sample size of 8,000, split evenly across these variables.

The core hypothesis posits that GPT-4 can simulate the cross-cultural distinctions in Big Five traits between subjects from the US and South Korea. Supplementary hypotheses test whether these differences can be maintained when using a single language inventory and explore the evolution in simulation ability between GPT-3.5 and GPT-4.

Key Results

The findings indicate that GPT-4 successfully replicates cross-cultural personality differences noted in previous studies. However, the simulated responses exhibit certain deficiencies. For instance, the simulations show an upward bias in mean scores with less variation compared to human data, and exhibit lower internal consistency and structural validity for certain personality dimensions.

Implications of Findings

The paper suggests that LLMs could potentially serve as valuable tools in cross-cultural research despite their current limitations. They offer a novel approach for simulating personality-related behaviors in varying cultural contexts, thereby supplementing traditional methodologies. Nevertheless, the upward bias and reduced variability in simulated responses highlight the need for cautious interpretation of LLM-generated data.

Future Directions for Research

Further studies could extend these findings by employing different personality frameworks like the HEXACO model or alternative inventories with robust invariance properties. Exploring additional cultural comparisons and incorporating demographic variables such as age and gender might also enrich the scope of research involving LLMs.

Additionally, employing other LLMs, including open-access models available via platforms like Hugging Face, might provide further insights and control over experimental conditions. Investigations into how stereotypes and Reinforcement Learning from Human Feedback (RLHF) might influence LLM outputs could clarify underlying mechanisms, potentially mitigating biases encoded in training data.

Concluding Remarks

This paper contributes to the intersection of AI and psychological sciences by exploring the capabilities of LLMs in mirroring nuanced human personality traits across cultures. While current models show promise, there is substantial room for improvement in terms of accuracy and variance representation. The results reinforce the need for interdisciplinary efforts to advance the use of AI in understanding and simulating human behavior, urging future iterations of LLMs to address extant limitations.