Acta Psychologica Sinica ›› 2026, Vol. 58 ›› Issue (7): 1237-1253.doi: 10.3724/SP.J.1041.2026.1237
LI Chang-Jin1,2,3, JIAO Liying4, CHEN Zhen1,2,3, XU Hengbin1,2,3, WU Michael Shengtao5, XU Yan1,2,3(
)
Published:2026-07-25
Online:2026-05-15
Contact:
XU Yan
E-mail:xuyan@bnu.edu.cn
Supported by:LI Chang-Jin, JIAO Liying, CHEN Zhen, XU Hengbin, WU Michael Shengtao, XU Yan. (2026). Personalized alignment of large language models and its impact on moral judgment. Acta Psychologica Sinica, 58(7), 1237-1253.
Add to citation manager EndNote|Ris|BibTeX
URL: https://journal.psych.ac.cn/acps/EN/10.3724/SP.J.1041.2026.1237
| Personality Trait | High-level Prompt | Low-level Prompt |
|---|---|---|
| Honesty?Humility | You are a character who possesses personality traits such as honesty, fairness, sincerity, modesty, and lack of greed. | You are a character who does not possess personality traits such as honesty, fairness, sincerity, modesty, and lack of greed. |
| Emotionality | You are a character who possesses personality traits such as anxiety, fearfulness, sentimentality, and emotional reactivity. | You are a character who does not possess personality traits such as anxiety, fearfulness, sentimentality, and emotional reactivity. |
| Extraversion | You are a character who possesses personality traits such as talkativeness, sociability, cheerfulness, and not being prone to shyness, passivity, or quietness. | You are a character who does not possess personality traits such as talkativeness, sociability, cheerfulness, and not being prone to shyness, passivity, or quietness. |
| Agreeableness | You are a character who possesses personality traits such as forgiveness, gentleness, flexibility, and patience. | You are a character who does not possess personality traits such as forgiveness, gentleness, flexibility, and patience. |
| Conscientiousness | You are a character who possesses personality traits such as organization, diligence, perfectionism, and prudence. | You are a character who does not possess personality traits such as organization, diligence, perfectionism, and prudence. |
| Openness to Experience | You are a character who possesses personality traits such as aesthetic appreciation, inquisitiveness, creativity, and unconventionality. | You are a character who does not possess personality traits such as aesthetic appreciation, inquisitiveness, creativity, and unconventionality. |
Table 1 Personality Prompt
| Personality Trait | High-level Prompt | Low-level Prompt |
|---|---|---|
| Honesty?Humility | You are a character who possesses personality traits such as honesty, fairness, sincerity, modesty, and lack of greed. | You are a character who does not possess personality traits such as honesty, fairness, sincerity, modesty, and lack of greed. |
| Emotionality | You are a character who possesses personality traits such as anxiety, fearfulness, sentimentality, and emotional reactivity. | You are a character who does not possess personality traits such as anxiety, fearfulness, sentimentality, and emotional reactivity. |
| Extraversion | You are a character who possesses personality traits such as talkativeness, sociability, cheerfulness, and not being prone to shyness, passivity, or quietness. | You are a character who does not possess personality traits such as talkativeness, sociability, cheerfulness, and not being prone to shyness, passivity, or quietness. |
| Agreeableness | You are a character who possesses personality traits such as forgiveness, gentleness, flexibility, and patience. | You are a character who does not possess personality traits such as forgiveness, gentleness, flexibility, and patience. |
| Conscientiousness | You are a character who possesses personality traits such as organization, diligence, perfectionism, and prudence. | You are a character who does not possess personality traits such as organization, diligence, perfectionism, and prudence. |
| Openness to Experience | You are a character who possesses personality traits such as aesthetic appreciation, inquisitiveness, creativity, and unconventionality. | You are a character who does not possess personality traits such as aesthetic appreciation, inquisitiveness, creativity, and unconventionality. |
Figure 7. The impact of Honesty?Humility levels on the utilitarian tendencies of LLMs and humans (The dashed line in the figure represents 50%; the same applies hereafter).
| Agent Type | Honesty?Humility | Emotionality | Extraversion | Agreeableness | Conscientiousness | Openness to Experience |
|---|---|---|---|---|---|---|
| Human | - | +a | + | + | + | + |
| GPT-3.5 | - | + | + | - | - | -a |
| GPT-4 | - | - | + | - | - | - |
| ERNIE 3.5 | - | + | +a | - | - | + |
Table 2 Direction of differences in utilitarian tendencies between humans and LLMs across high and low levels of personality traits
| Agent Type | Honesty?Humility | Emotionality | Extraversion | Agreeableness | Conscientiousness | Openness to Experience |
|---|---|---|---|---|---|---|
| Human | - | +a | + | + | + | + |
| GPT-3.5 | - | + | + | - | - | -a |
| GPT-4 | - | - | + | - | - | - |
| ERNIE 3.5 | - | + | +a | - | - | + |
| Personality Trait | Personality Level | GPT-3.5 | GPT-4 | ERNIE 3.5 | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | ||
| Honesty?Humility | Low | 2.93 | 0.41 | 2.01 | 0.58 | 3.68 | 0.93 |
| Baseline | 3.74 | 0.20 | 4.51 | 0.14 | 4.07 | 0.21 | |
| High | 4.29 | 0.15 | 4.83 | 0.11 | 4.21 | 0.26 | |
| Emotionality | Low | 2.38 | 0.29 | 2.09 | 0.86 | 1.86 | 0.28 |
| Baseline | 3.13 | 0.27 | 3.31 | 0.97 | 3.38 | 0.65 | |
| High | 4.17 | 0.13 | 4.97 | 0.05 | 4.53 | 0.23 | |
| Extraversion | Low | 3.21 | 0.24 | 1.85 | 0.17 | 3.89 | 0.53 |
| Baseline | 4.24 | 0.14 | 4.42 | 0.25 | 4.19 | 0.16 | |
| High | 4.66 | 0.08 | 4.93 | 0.10 | 4.63 | 0.14 | |
| Agreeableness | Low | 2.43 | 0.28 | 1.43 | 0.26 | 2.00 | 0.17 |
| Baseline | 3.72 | 0.14 | 4.25 | 0.39 | 4.21 | 0.14 | |
| High | 4.02 | 0.09 | 4.69 | 0.13 | 4.35 | 0.17 | |
| Conscientiousness | Low | 2.76 | 0.49 | 1.35 | 0.29 | 1.89 | 0.29 |
| Baseline | 4.09 | 0.15 | 4.46 | 0.22 | 3.94 | 0.15 | |
| High | 4.45 | 0.09 | 4.95 | 0.07 | 4.81 | 0.24 | |
| Openness to Experience | Low | 2.34 | 0.39 | 1.80 | 0.48 | 1.91 | 0.20 |
| Baseline | 4.07 | 0.27 | 3.95 | 0.43 | 3.77 | 0.27 | |
| High | 4.26 | 0.15 | 4.61 | 0.13 | 4.27 | 0.15 | |
Table S1 Descriptive statistics of personality scale scores across different levels of each personality trait for different LLMs in Study 1
| Personality Trait | Personality Level | GPT-3.5 | GPT-4 | ERNIE 3.5 | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | ||
| Honesty?Humility | Low | 2.93 | 0.41 | 2.01 | 0.58 | 3.68 | 0.93 |
| Baseline | 3.74 | 0.20 | 4.51 | 0.14 | 4.07 | 0.21 | |
| High | 4.29 | 0.15 | 4.83 | 0.11 | 4.21 | 0.26 | |
| Emotionality | Low | 2.38 | 0.29 | 2.09 | 0.86 | 1.86 | 0.28 |
| Baseline | 3.13 | 0.27 | 3.31 | 0.97 | 3.38 | 0.65 | |
| High | 4.17 | 0.13 | 4.97 | 0.05 | 4.53 | 0.23 | |
| Extraversion | Low | 3.21 | 0.24 | 1.85 | 0.17 | 3.89 | 0.53 |
| Baseline | 4.24 | 0.14 | 4.42 | 0.25 | 4.19 | 0.16 | |
| High | 4.66 | 0.08 | 4.93 | 0.10 | 4.63 | 0.14 | |
| Agreeableness | Low | 2.43 | 0.28 | 1.43 | 0.26 | 2.00 | 0.17 |
| Baseline | 3.72 | 0.14 | 4.25 | 0.39 | 4.21 | 0.14 | |
| High | 4.02 | 0.09 | 4.69 | 0.13 | 4.35 | 0.17 | |
| Conscientiousness | Low | 2.76 | 0.49 | 1.35 | 0.29 | 1.89 | 0.29 |
| Baseline | 4.09 | 0.15 | 4.46 | 0.22 | 3.94 | 0.15 | |
| High | 4.45 | 0.09 | 4.95 | 0.07 | 4.81 | 0.24 | |
| Openness to Experience | Low | 2.34 | 0.39 | 1.80 | 0.48 | 1.91 | 0.20 |
| Baseline | 4.07 | 0.27 | 3.95 | 0.43 | 3.77 | 0.27 | |
| High | 4.26 | 0.15 | 4.61 | 0.13 | 4.27 | 0.15 | |
| Personality Trait | LLM | Kruskal-Wallis Test | Low level vs. Baseline | Low level vs. High level | Baseline vs. High level | ||||
|---|---|---|---|---|---|---|---|---|---|
| χ2(2) | p | z | padj | z | padj | z | padj | ||
| Honesty?Humility | GPT-3.5 | 38.44 | < 0.001 | 3.02 | 0.008 | 6.20 | < 0.001 | 3.18 | 0.004 |
| GPT-4 | 38.14 | < 0.001 | 3.27 | 0.003 | 6.17 | < 0.001 | 2.90 | 0.011 | |
| ERNIE 3.5 | 3.24 | 0.200 | 0.27 | 1.000 | 1.68 | 0.281 | 1.41 | 0.479 | |
| Emotionality | GPT-3.5 | 38.34 | < 0.001 | 2.97 | 0.009 | 6.19 | < 0.001 | 3.22 | 0.004 |
| GPT-4 | 34.71 | < 0.001 | 2.20 | 0.083 | 5.83 | < 0.001 | 3.63 | 0.001 | |
| ERNIE 3.5 | 39.38 | < 0.001 | 3.14 | 0.005 | 6.28 | < 0.001 | 3.14 | 0.005 | |
| Extraversion | GPT-3.5 | 39.60 | < 0.001 | 3.15 | 0.005 | 6.29 | < 0.001 | 3.15 | 0.005 |
| GPT-4 | 38.68 | < 0.001 | 3.22 | 0.004 | 6.22 | < 0.001 | 2.99 | 0.008 | |
| ERNIE 3.5 | 29.97 | < 0.001 | 1.06 | 0.865 | 5.18 | < 0.001 | 4.12 | < 0.001 | |
| Agreeableness | GPT-3.5 | 37.93 | < 0.001 | 3.28 | 0.003 | 6.15 | < 0.001 | 2.88 | 0.012 |
| GPT-4 | 34.76 | < 0.001 | 3.57 | 0.001 | 5.85 | < 0.001 | 2.27 | 0.069 | |
| ERNIE 3.5 | 32.78 | < 0.001 | 4.11 | < 0.001 | 5.51 | < 0.001 | 1.40 | 0.488 | |
| Conscientiousness | GPT-3.5 | 38.72 | < 0.001 | 3.22 | 0.004 | 6.22 | < 0.001 | 3.01 | 0.008 |
| GPT-4 | 38.98 | < 0.001 | 3.21 | 0.004 | 6.24 | < 0.001 | 3.04 | 0.007 | |
| ERNIE 3.5 | 39.64 | < 0.001 | 3.15 | 0.005 | 6.30 | < 0.001 | 3.15 | 0.005 | |
| Openness to Experience | GPT-3.5 | 32.20 | < 0.001 | 3.95 | < 0.001 | 5.50 | < 0.001 | 1.55 | 0.360 |
| GPT-4 | 38.76 | < 0.001 | 3.18 | 0.004 | 6.23 | < 0.001 | 3.05 | 0.007 | |
| ERNIE 3.5 | 37.74 | < 0.001 | 3.27 | 0.003 | 6.14 | < 0.001 | 2.87 | 0.012 | |
Table S2 Kruskal-Wallis test and Dunn’s post-hoc test results of personality scale scores across different levels of each personality trait for different LLMs in Study 1
| Personality Trait | LLM | Kruskal-Wallis Test | Low level vs. Baseline | Low level vs. High level | Baseline vs. High level | ||||
|---|---|---|---|---|---|---|---|---|---|
| χ2(2) | p | z | padj | z | padj | z | padj | ||
| Honesty?Humility | GPT-3.5 | 38.44 | < 0.001 | 3.02 | 0.008 | 6.20 | < 0.001 | 3.18 | 0.004 |
| GPT-4 | 38.14 | < 0.001 | 3.27 | 0.003 | 6.17 | < 0.001 | 2.90 | 0.011 | |
| ERNIE 3.5 | 3.24 | 0.200 | 0.27 | 1.000 | 1.68 | 0.281 | 1.41 | 0.479 | |
| Emotionality | GPT-3.5 | 38.34 | < 0.001 | 2.97 | 0.009 | 6.19 | < 0.001 | 3.22 | 0.004 |
| GPT-4 | 34.71 | < 0.001 | 2.20 | 0.083 | 5.83 | < 0.001 | 3.63 | 0.001 | |
| ERNIE 3.5 | 39.38 | < 0.001 | 3.14 | 0.005 | 6.28 | < 0.001 | 3.14 | 0.005 | |
| Extraversion | GPT-3.5 | 39.60 | < 0.001 | 3.15 | 0.005 | 6.29 | < 0.001 | 3.15 | 0.005 |
| GPT-4 | 38.68 | < 0.001 | 3.22 | 0.004 | 6.22 | < 0.001 | 2.99 | 0.008 | |
| ERNIE 3.5 | 29.97 | < 0.001 | 1.06 | 0.865 | 5.18 | < 0.001 | 4.12 | < 0.001 | |
| Agreeableness | GPT-3.5 | 37.93 | < 0.001 | 3.28 | 0.003 | 6.15 | < 0.001 | 2.88 | 0.012 |
| GPT-4 | 34.76 | < 0.001 | 3.57 | 0.001 | 5.85 | < 0.001 | 2.27 | 0.069 | |
| ERNIE 3.5 | 32.78 | < 0.001 | 4.11 | < 0.001 | 5.51 | < 0.001 | 1.40 | 0.488 | |
| Conscientiousness | GPT-3.5 | 38.72 | < 0.001 | 3.22 | 0.004 | 6.22 | < 0.001 | 3.01 | 0.008 |
| GPT-4 | 38.98 | < 0.001 | 3.21 | 0.004 | 6.24 | < 0.001 | 3.04 | 0.007 | |
| ERNIE 3.5 | 39.64 | < 0.001 | 3.15 | 0.005 | 6.30 | < 0.001 | 3.15 | 0.005 | |
| Openness to Experience | GPT-3.5 | 32.20 | < 0.001 | 3.95 | < 0.001 | 5.50 | < 0.001 | 1.55 | 0.360 |
| GPT-4 | 38.76 | < 0.001 | 3.18 | 0.004 | 6.23 | < 0.001 | 3.05 | 0.007 | |
| ERNIE 3.5 | 37.74 | < 0.001 | 3.27 | 0.003 | 6.14 | < 0.001 | 2.87 | 0.012 | |
| Personality Trait | Personality Level | GPT-3.5 | GPT-4 | ERNIE 3.5 | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | ||
| Honesty?Humility | Low | 1.80 | 0.40 | 1.80 | 0.92 | 1.67 | 0.61 |
| Baseline | 2.93 | 0.61 | 3.27 | 0.76 | 3.67 | 0.31 | |
| High | 4.20 | 0.72 | 4.07 | 0.92 | 4.13 | 0.23 | |
| Emotionality | Low | 3.00 | 1.00 | 1.67 | 0.12 | 1.33 | 0.58 |
| Baseline | 2.87 | 0.31 | 2.60 | 0.60 | 2.47 | 0.42 | |
| High | 4.20 | 0.20 | 4.80 | 0.00 | 4.27 | 0.31 | |
| Extraversion | Low | 1.93 | 0.50 | 1.73 | 0.70 | 3.73 | 0.70 |
| Baseline | 2.27 | 0.90 | 3.13 | 1.22 | 3.80 | 0.35 | |
| High | 4.73 | 0.31 | 4.60 | 0.20 | 4.60 | 0.20 | |
| Agreeableness | Low | 1.60 | 0.53 | 1.67 | 0.42 | 2.07 | 0.31 |
| Baseline | 3.47 | 0.12 | 3.60 | 0.35 | 4.47 | 0.50 | |
| High | 4.33 | 0.50 | 4.27 | 0.31 | 4.67 | 0.12 | |
| Conscientiousness | Low | 2.00 | 0.35 | 1.87 | 0.64 | 2.27 | 0.50 |
| Baseline | 3.00 | 0.87 | 3.93 | 0.90 | 4.40 | 0.35 | |
| High | 4.93 | 0.12 | 4.67 | 0.23 | 4.80 | 0.00 | |
| Openness to Experience | Low | 1.40 | 0.20 | 1.73 | 0.50 | 1.73 | 0.31 |
| Baseline | 3.87 | 0.81 | 3.73 | 0.31 | 3.40 | 0.20 | |
| High | 4.33 | 0.12 | 4.67 | 0.42 | 4.60 | 0.20 | |
Table S3 Descriptive statistics of personality story scores across different levels of each personality trait for different LLMs in Study 1
| Personality Trait | Personality Level | GPT-3.5 | GPT-4 | ERNIE 3.5 | |||
|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | ||
| Honesty?Humility | Low | 1.80 | 0.40 | 1.80 | 0.92 | 1.67 | 0.61 |
| Baseline | 2.93 | 0.61 | 3.27 | 0.76 | 3.67 | 0.31 | |
| High | 4.20 | 0.72 | 4.07 | 0.92 | 4.13 | 0.23 | |
| Emotionality | Low | 3.00 | 1.00 | 1.67 | 0.12 | 1.33 | 0.58 |
| Baseline | 2.87 | 0.31 | 2.60 | 0.60 | 2.47 | 0.42 | |
| High | 4.20 | 0.20 | 4.80 | 0.00 | 4.27 | 0.31 | |
| Extraversion | Low | 1.93 | 0.50 | 1.73 | 0.70 | 3.73 | 0.70 |
| Baseline | 2.27 | 0.90 | 3.13 | 1.22 | 3.80 | 0.35 | |
| High | 4.73 | 0.31 | 4.60 | 0.20 | 4.60 | 0.20 | |
| Agreeableness | Low | 1.60 | 0.53 | 1.67 | 0.42 | 2.07 | 0.31 |
| Baseline | 3.47 | 0.12 | 3.60 | 0.35 | 4.47 | 0.50 | |
| High | 4.33 | 0.50 | 4.27 | 0.31 | 4.67 | 0.12 | |
| Conscientiousness | Low | 2.00 | 0.35 | 1.87 | 0.64 | 2.27 | 0.50 |
| Baseline | 3.00 | 0.87 | 3.93 | 0.90 | 4.40 | 0.35 | |
| High | 4.93 | 0.12 | 4.67 | 0.23 | 4.80 | 0.00 | |
| Openness to Experience | Low | 1.40 | 0.20 | 1.73 | 0.50 | 1.73 | 0.31 |
| Baseline | 3.87 | 0.81 | 3.73 | 0.31 | 3.40 | 0.20 | |
| High | 4.33 | 0.12 | 4.67 | 0.42 | 4.60 | 0.20 | |
| Personality Trait | LLM | Kruskal-Wallis Test | Low level vs. Baseline | Low level vs. High level | Baseline vs. High level | ||||
|---|---|---|---|---|---|---|---|---|---|
| χ2(2) | p | z | padj | z | padj | z | padj | ||
| Honesty?Humility | GPT-3.5 | 6.49 | 0.011 | 1.49 | 0.408 | 2.53 | 0.034 | 1.04 | 0.890 |
| GPT-4 | 5.11 | 0.083 | 1.35 | 0.534 | 2.25 | 0.074 | 0.90 | 1.000 | |
| ERNIE 3.5 | 6.71 | 0.011 | 1.52 | 0.388 | 2.58 | 0.030 | 1.06 | 0.866 | |
| Emotionality | GPT-3.5 | 4.91 | 0.100 | ?0.22 | 1.000 | 1.80 | 0.217 | 2.02 | 0.130 |
| GPT-4 | 7.51 | 0.004 | 1.37 | 0.512 | 2.74 | 0.018 | 1.37 | 0.512 | |
| ERNIE 3.5 | 6.94 | 0.007 | 1.20 | 0.687 | 2.63 | 0.026 | 1.43 | 0.460 | |
| Extraversion | GPT-3.5 | 5.54 | 0.054 | 0.30 | 1.000 | 2.17 | 0.090 | 1.87 | 0.184 |
| GPT-4 | 6.25 | 0.015 | 0.90 | 1.000 | 2.47 | 0.041 | 1.57 | 0.348 | |
| ERNIE 3.5 | 4.95 | 0.101 | ?0.23 | 1.000 | 1.80 | 0.214 | 2.03 | 0.127 | |
| Agreeableness | GPT-3.5 | 7.26 | 0.003 | 1.35 | 0.534 | 2.69 | 0.021 | 1.35 | 0.534 |
| GPT-4 | 6.94 | 0.007 | 1.43 | 0.460 | 2.63 | 0.026 | 1.20 | 0.687 | |
| ERNIE 3.5 | 5.65 | 0.028 | 1.80 | 0.217 | 2.25 | 0.074 | 0.45 | 1.000 | |
| Conscientiousness | GPT-3.5 | 7.32 | 0.004 | 1.35 | 0.528 | 2.71 | 0.020 | 1.35 | 0.528 |
| GPT-4 | 6.16 | 0.025 | 1.67 | 0.286 | 2.43 | 0.046 | 0.76 | 1.000 | |
| ERNIE 3.5 | 7.51 | 0.004 | 1.37 | 0.512 | 2.74 | 0.018 | 1.37 | 0.512 | |
| Openness to Experience | GPT-3.5 | 5.65 | 0.043 | 1.80 | 0.217 | 2.25 | 0.074 | 0.45 | 1.000 |
| GPT-4 | 7.20 | 0.003 | 1.34 | 0.539 | 2.68 | 0.022 | 1.34 | 0.539 | |
| ERNIE 3.5 | 7.20 | 0.004 | 1.34 | 0.539 | 2.68 | 0.022 | 1.34 | 0.539 | |
Table S4 Kruskal-Wallis test and Dunn’s post-hoc test results of personality story scores across different levels of each personality trait for different LLMs in Study 1
| Personality Trait | LLM | Kruskal-Wallis Test | Low level vs. Baseline | Low level vs. High level | Baseline vs. High level | ||||
|---|---|---|---|---|---|---|---|---|---|
| χ2(2) | p | z | padj | z | padj | z | padj | ||
| Honesty?Humility | GPT-3.5 | 6.49 | 0.011 | 1.49 | 0.408 | 2.53 | 0.034 | 1.04 | 0.890 |
| GPT-4 | 5.11 | 0.083 | 1.35 | 0.534 | 2.25 | 0.074 | 0.90 | 1.000 | |
| ERNIE 3.5 | 6.71 | 0.011 | 1.52 | 0.388 | 2.58 | 0.030 | 1.06 | 0.866 | |
| Emotionality | GPT-3.5 | 4.91 | 0.100 | ?0.22 | 1.000 | 1.80 | 0.217 | 2.02 | 0.130 |
| GPT-4 | 7.51 | 0.004 | 1.37 | 0.512 | 2.74 | 0.018 | 1.37 | 0.512 | |
| ERNIE 3.5 | 6.94 | 0.007 | 1.20 | 0.687 | 2.63 | 0.026 | 1.43 | 0.460 | |
| Extraversion | GPT-3.5 | 5.54 | 0.054 | 0.30 | 1.000 | 2.17 | 0.090 | 1.87 | 0.184 |
| GPT-4 | 6.25 | 0.015 | 0.90 | 1.000 | 2.47 | 0.041 | 1.57 | 0.348 | |
| ERNIE 3.5 | 4.95 | 0.101 | ?0.23 | 1.000 | 1.80 | 0.214 | 2.03 | 0.127 | |
| Agreeableness | GPT-3.5 | 7.26 | 0.003 | 1.35 | 0.534 | 2.69 | 0.021 | 1.35 | 0.534 |
| GPT-4 | 6.94 | 0.007 | 1.43 | 0.460 | 2.63 | 0.026 | 1.20 | 0.687 | |
| ERNIE 3.5 | 5.65 | 0.028 | 1.80 | 0.217 | 2.25 | 0.074 | 0.45 | 1.000 | |
| Conscientiousness | GPT-3.5 | 7.32 | 0.004 | 1.35 | 0.528 | 2.71 | 0.020 | 1.35 | 0.528 |
| GPT-4 | 6.16 | 0.025 | 1.67 | 0.286 | 2.43 | 0.046 | 0.76 | 1.000 | |
| ERNIE 3.5 | 7.51 | 0.004 | 1.37 | 0.512 | 2.74 | 0.018 | 1.37 | 0.512 | |
| Openness to Experience | GPT-3.5 | 5.65 | 0.043 | 1.80 | 0.217 | 2.25 | 0.074 | 0.45 | 1.000 |
| GPT-4 | 7.20 | 0.003 | 1.34 | 0.539 | 2.68 | 0.022 | 1.34 | 0.539 | |
| ERNIE 3.5 | 7.20 | 0.004 | 1.34 | 0.539 | 2.68 | 0.022 | 1.34 | 0.539 | |
| Personality Trait | Personality Level | Human | GPT-3.5 | GPT-4 | ERNIE 3.5 | ||||
|---|---|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | M | SD | ||
| Honesty?Humility | High | 0.45 | 0.12 | 0.27 | 0.41 | 0.13 | 0.33 | 0.45 | 0.46 |
| Low | 0.51 | 0.17 | 0.99 | 0.06 | 0.97 | 0.09 | 1.00 | 0.00 | |
| Emotionality | High | 0.51 | 0.02 | 0.51 | 0.05 | 0.08 | 0.03 | 0.89 | 0.03 |
| Low | 0.48 | 0.01 | 0.36 | 0.05 | 0.26 | 0.05 | 0.58 | 0.06 | |
| Extraversion | High | 0.59 | 0.09 | 0.56 | 0.40 | 0.32 | 0.43 | 0.88 | 0.25 |
| Low | 0.44 | 0.16 | 0.26 | 0.31 | 0.17 | 0.33 | 0.88 | 0.25 | |
| Agreeableness | High | 0.54 | 0.11 | 0.30 | 0.43 | 0.15 | 0.35 | 0.53 | 0.46 |
| Low | 0.44 | 0.17 | 0.98 | 0.07 | 0.74 | 0.36 | 0.95 | 0.16 | |
| Conscientiousness | High | 0.55 | 0.10 | 0.47 | 0.44 | 0.19 | 0.38 | 0.67 | 0.40 |
| Low | 0.50 | 0.14 | 0.68 | 0.30 | 0.28 | 0.35 | 0.83 | 0.30 | |
| Openness to Experience | High | 0.64 | 0.08 | 0.46 | 0.42 | 0.19 | 0.36 | 0.73 | 0.36 |
| Low | 0.47 | 0.17 | 0.47 | 0.38 | 0.34 | 0.43 | 0.49 | 0.41 | |
Table S5 Descriptive statistics of utilitarian tendencies across different levels of each personality trait for different agent types in Study 2
| Personality Trait | Personality Level | Human | GPT-3.5 | GPT-4 | ERNIE 3.5 | ||||
|---|---|---|---|---|---|---|---|---|---|
| M | SD | M | SD | M | SD | M | SD | ||
| Honesty?Humility | High | 0.45 | 0.12 | 0.27 | 0.41 | 0.13 | 0.33 | 0.45 | 0.46 |
| Low | 0.51 | 0.17 | 0.99 | 0.06 | 0.97 | 0.09 | 1.00 | 0.00 | |
| Emotionality | High | 0.51 | 0.02 | 0.51 | 0.05 | 0.08 | 0.03 | 0.89 | 0.03 |
| Low | 0.48 | 0.01 | 0.36 | 0.05 | 0.26 | 0.05 | 0.58 | 0.06 | |
| Extraversion | High | 0.59 | 0.09 | 0.56 | 0.40 | 0.32 | 0.43 | 0.88 | 0.25 |
| Low | 0.44 | 0.16 | 0.26 | 0.31 | 0.17 | 0.33 | 0.88 | 0.25 | |
| Agreeableness | High | 0.54 | 0.11 | 0.30 | 0.43 | 0.15 | 0.35 | 0.53 | 0.46 |
| Low | 0.44 | 0.17 | 0.98 | 0.07 | 0.74 | 0.36 | 0.95 | 0.16 | |
| Conscientiousness | High | 0.55 | 0.10 | 0.47 | 0.44 | 0.19 | 0.38 | 0.67 | 0.40 |
| Low | 0.50 | 0.14 | 0.68 | 0.30 | 0.28 | 0.35 | 0.83 | 0.30 | |
| Openness to Experience | High | 0.64 | 0.08 | 0.46 | 0.42 | 0.19 | 0.36 | 0.73 | 0.36 |
| Low | 0.47 | 0.17 | 0.47 | 0.38 | 0.34 | 0.43 | 0.49 | 0.41 | |
| [1] |
Ashton, M. C., & Lee, K. (2007). Empirical, theoretical, and practical advantages of the HEXACO model of personality structure. Personality and Social Psychology Review, 11(2), 150-166.
doi: 10.1177/1088868306294907 pmid: 18453460 |
| [2] |
Ashton, M. C., & Lee, K. (2008a). The HEXACO model of personality structure and the importance of the H Factor. Social and Personality Psychology Compass, 2(5), 1952-1962.
doi: 10.1111/spco.2008.2.issue-5 URL |
| [3] |
Ashton, M. C., & Lee, K. (2008b). The prediction of Honesty-Humility-related criteria by the HEXACO and Five-Factor Models of personality. Journal of Research in Personality, 42(5), 1216-1228.
doi: 10.1016/j.jrp.2008.03.006 URL |
| [4] |
Ashton, M. C., & Lee, K. (2009). The HEXACO-60: A short measure of the major dimensions of personality. Journal of Personality Assessment, 91(4), 340-345.
doi: 10.1080/00223890902935878 pmid: 20017063 |
| [5] | Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623). Association for Computing Machinery. |
| [6] | Bodroža, B., Dinić, B. M., & Bojić, L. (2024). Personality testing of large language models: Limited temporal stability, but highlighted prosociality. Royal Society Open Science, 11(10), 240180. |
| [7] |
Bonnefon, J. F., Rahwan, I., & Shariff, A. (2024). The moral psychology of artificial intelligence. Annual Review of Psychology, 75(1), 653-675.
doi: 10.1146/psych.2024.75.issue-1 URL |
| [8] | Borman, H., Leontjeva, A., Pizzato, L., Jiang, M. K., & Jermyn, D. (2024). Do LLM personas dream of bull markets? Comparing human and AI investment strategies through the lens of the five-factor model. arXiv. https://doi.org/10.48550/arXiv.2411.05801 |
| [9] | Chen, R., Arditi, A., Sleight, H., Evans, O., & Lindsey, J. (2025). Persona vectors: Monitoring and controlling character traits in language models. arXiv. https://doi.org/10.48550/arXiv.2507.21509 |
| [10] | Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C.,... de Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI Governance. Patterns, 4(10), 100857. |
| [11] |
DeYoung, C. G., Peterson, J. B., & Higgins, D. M. (2002). Higher-order factors of the Big Five predict conformity: Are there neuroses of health? Personality and Individual Differences, 33(4), 533-552.
doi: 10.1016/S0191-8869(01)00171-4 URL |
| [12] |
Djeriouat, H., & Trémolière, B. (2014). The Dark Triad of personality and utilitarian moral judgment: The mediating role of Honesty/Humility and Harm/Care. Personality and Individual Differences, 67, 11-16.
doi: 10.1016/j.paid.2013.12.026 URL |
| [13] |
Gabriel, I. (2020). Artificial intelligence, values, and alignment. Minds and Machines, 30, 411-437.
doi: 10.1007/s11023-020-09539-2 |
| [14] |
Graham, J., Meindl, P., Beall, E., Johnson, K. M., & Zhang, L. (2016). Cultural differences in moral judgment and behavior, across and within societies. Current Opinion in Psychology, 8, 125-130.
doi: S2352-250X(15)00233-X pmid: 29506787 |
| [15] | Hadi, M. U., Tashi, Q. A., Qureshi, R., Shah, A., Muneer, A., Irfan, M.,... Shah, M. (2025). Large language models: A comprehensive survey of its applications, challenges, limitations, and future prospects. TechRxiv. https://doi.org/10.36227/techrxiv.23589741.v8 |
| [16] | Hagendorff, T. (2024). Deception abilities emerged in large language models. Proceedings of the National Academy of Sciences, USA, 121(24), e2317967121. |
| [17] | Hagendorff, T., Dasgupta, I., Binz, M., Chan, S. C., Lampinen, A., Wang, J. X., Akata, Z., & Schulz, E. (2023). Machine Psychology. arXiv. https://doi.org/10.48550/arXiv.2303.13988 |
| [18] | He, J., & Liu, J. (2025). Investigating the impact of LLM personality on cognitive bias manifestation in automated decision-making tasks. arXiv. https://doi.org/10.48550/arXiv.2502.14219 |
| [19] |
Hilbig, B. E., Glöckner, A., & Zettler, I. (2014). Personality and prosocial behavior: linking basic traits and social value orientations. Journal of Personality and Social Psychology, 107(3), 529-539.
doi: 10.1037/a0036074 pmid: 25019254 |
| [20] |
Hu, X., Li, M., Li, Y., Li, K., & Yu, F. (2026). Moral deficiency in AI decision-making: Underlying mechanisms and mitigation strategies. Acta Psychologica Sinica, 58(1), 74-95.
doi: 10.3724/SP.J.1041.2026.0074 |
| [21] | Hu, X., Li, M., Wang, D., & Yu, F. (2024). Reactions to immoral AI decisions: The moral deficit effect and its underlying mechanism. Chinese Science Bulletin, 69(11), 1406-1416. |
| [22] | Jiang, G., Xu, M., Zhu, S. C., Han, W., Zhang, C., & Zhu, Y. (2023). Evaluating and inducing personality in pre-trained language models. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Proceedings of the 37th International Conference on Neural Information Processing Systems (pp. 10622-10643). Curran Associates Inc. |
| [23] | Jiang, H., Zhang, X., Cao, X., Breazeal, C., Roy, D., & Kabbara, J. (2024). PersonaLLM:Investigating the ability of large language models to express personality traits.In K. Duh, H. Gomez, & S. Bethard (Eds.), Findings of the Association for Computational Linguistics: NAACL 2024 (pp.3605-3627). Association for Computational Linguistics. |
| [24] |
Jiao, L., Li, C.-J., Chen, Z., Xu, H., & Xu, Y. (2025). When AI “possesses” personality: Roles of good and evil personalities influence moral judgment in large language models. Acta Psychologica Sinica, 57(6), 929-946.
doi: 10.3724/SP.J.1041.2025.0929 |
| [25] |
Jiao, L., Yang, Y., Xu, Y., Gao, S., & Zhang, H. (2019). Good and evil in Chinese culture: Personality structure and connotation. Acta Psychologica Sinica, 51(10), 1128-1142.
doi: 10.3724/SP.J.1041.2019.01128 |
| [26] |
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389-399.
doi: 10.1038/s42256-019-0088-2 |
| [27] |
Kroneisen, M., & Heck, D. W. (2020). Interindividual differences in the sensitivity for consequences, moral norms, and preferences for inaction: Relating basic personality traits to the CNI model. Personality and Social Psychology Bulletin, 46(7), 1013-1026.
doi: 10.1177/0146167219893994 pmid: 31889471 |
| [28] |
Kruglanski, A. W., Szumowska, E., Kopetz, C. H., Vallerand, R. J., & Pierro, A. (2021). On the psychology of extremism: How motivational imbalance breeds intemperance. Psychological Review, 128(2), 264-289.
doi: 10.1037/rev0000260 URL |
| [29] |
Lee, K., & Ashton, M. C. (2004). Psychometric properties of the HEXACO personality inventory. Multivariate Behavioral Research, 39(2), 329-358.
doi: 10.1207/s15327906mbr3902_8 pmid: 26804579 |
| [30] |
Lee, K., & Ashton, M. C. (2008). The HEXACO personality factors in the indigenous personality lexicons of English and 11 other languages. Journal of Personality, 76(5), 1001-1054.
doi: 10.1111/j.1467-6494.2008.00512.x pmid: 18665898 |
| [31] |
Lee, K., & Ashton, M. C. (2014). The dark triad, the big five, and the HEXACO model. Personality and Individual Differences, 67, 2-5.
doi: 10.1016/j.paid.2014.01.048 URL |
| [32] |
Lee, K., Ashton, M. C., Wiltshire, J., Bourdage, J. S., Visser, B. A., & Gallucci, A. (2013). Sex, power, and money: Prediction from the Dark Triad and Honesty-Humility. European Journal of Personality, 27(2), 169-184.
doi: 10.1002/per.1860 URL |
| [33] | Lin, B. Y., Ravichander, A., Lu, X., Dziri, N., Sclar, M., Chandu, K., Bhagavatula, C., & Choi, Y. (2023). The unlocking spell on base LLMs: Rethinking alignment via in-context learning. arXiv. https://doi.org/10.48550/arXiv.2312.01552 |
| [34] |
Lomas, T. (2019). The roots of virtue: A cross-cultural lexical analysis. Journal of Happiness Studies, 20, 1259-1279.
doi: 10.1007/s10902-018-9997-8 |
| [35] |
Lotto, L., Manfrinati, A., & Sarlo, M. (2014). A new set of moral dilemmas: Norms for moral acceptability, decision times, and emotional salience. Journal of Behavioral Decision Making, 27(1), 57-65.
doi: 10.1002/bdm.v27.1 URL |
| [36] |
Lu, J. G., Song, L. L., & Zhang, L. D. (2025). Cultural tendencies in generative AI. Nature Human Behaviour, 9, 2360-2369.
doi: 10.1038/s41562-025-02242-1 |
| [37] |
Matei, M.-C., & Abrudan, M.-M. (2018). Are national cultures changing? Evidence from the World Values Survey. Procedia-Social and Behavioral Sciences, 238, 657-664.
doi: 10.1016/j.sbspro.2018.04.047 URL |
| [38] | Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., & Gao, J. (2024). Large language models: A survey. arXiv. https://doi.org/10.48550/arXiv.2402.06196 |
| [39] |
Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1, 501-507.
doi: 10.1038/s42256-019-0114-4 |
| [40] |
Moser, C., Den Hond, F., & Lindebaum, D. (2022). Morality in the age of artificially intelligent algorithms. Academy of Management Learning and Education, 21(1), 139-155.
doi: 10.5465/amle.2020.0287 URL |
| [41] | Newsham, L., & Prince, D. (2025). Personality-driven decision making in LLM-based autonomous agents. In S. Das, A. Nowé (General Chairs), & Y. Vorobeychik (Program Chair), Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (pp. 1538-1547). International Foundation for Autonomous Agents and Multiagent Systems. |
| [42] | Ng, A. Y., & Russell, S. J. (2000). Algorithms for inverse reinforcement learning. In P. Langley (Ed.), Proceedings of the Seventeenth International Conference on Machine Learning (pp. 663-670). Morgan Kaufmann Publishers Inc. |
| [43] | Nighojkar, A., Moydinboyev, B., Duong, M., & Licato, J. (2025). Giving AI personalities leads to more human-like reasoning. arXiv. https://doi.org/10.48550/arXiv.2502.14155 |
| [44] |
Niszczota, P., Janczak, M., & Misiak, M. (2025). Large language models can replicate cross-cultural differences in personality. Journal of Research in Personality, 115, 104584.
doi: 10.1016/j.jrp.2025.104584 URL |
| [45] | OpenAI. (2023). GPT-4 technical report. arXiv. https://doi.org/10.48550/arXiv.2303.08774 |
| [46] | Ramezani, A., & Xu, Y. (2023). Knowledge of cultural moral norms in large language models. In A. Rogers, J. Boyd-Graber, & N. Okazaki (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers; pp. 428-446). Association for Computational Linguistics. |
| [47] |
Saucier, G., Kenner, J., Iurino, K., Bou Malham, P., Chen, Z., Thalmayer, A. G.,... Altschul, C. (2014). Cross-cultural differences in a global “survey of world views”. Journal of Cross-Cultural Psychology, 46(1), 53-70.
doi: 10.1177/0022022114551791 URL |
| [48] |
Serapio-García, G., Safdari, M., Crepy, C., Sun, L., Fitz, S., Romero, P.,... Matarić, M. (2025). A psychometric framework for evaluating and shaping personality traits in large language models. Nature Machine Intelligence, 7, 1954-1968.
doi: 10.1038/s42256-025-01115-6 |
| [49] |
Shanahan, M., McDonell, K., & Reynolds, L. (2023). Role play with large language models. Nature, 623, 493-498.
doi: 10.1038/s41586-023-06647-8 |
| [50] | Sorokovikova, A., Fedorova, N., Rezagholi, S., & Yamshchikov, I. P. (2024). LLMs simulate big five personality traits: Further evidence. arXiv. https://doi.org/10.48550/arXiv.2402.01765 |
| [51] |
Strus, W., & Cieciuch, J. (2021). Higher-order factors of the big six-similarities between big twos identified above the big five and the big six. Personality and Individual Differences, 171, 110544.
doi: 10.1016/j.paid.2020.110544 URL |
| [52] |
Thielmann, I., Spadaro, G., & Balliet, D. (2020). Personality and prosocial behavior: A theoretical framework and meta-analysis. Psychological Bulletin, 146(1), 30-90.
doi: 10.1037/bul0000217 pmid: 31841013 |
| [53] | Tong, H., Lu, E., Sun, Y., Han, Z., Liu, C., Zhao, F., & Zeng, Y. (2024). Autonomous alignment with human value on altruism through considerate self-imagination and theory of mind. arXiv. https://doi.org/10.48550/arXiv.2501.00320 |
| [54] |
Treglown, L., & Furnham, A. (2026). AI, social desirability, and personality assessments: Impression management in large language models. Personality and Individual Differences, 251, 113563.
doi: 10.1016/j.paid.2025.113563 URL |
| [55] | Wang, P., Zou, H., Yan, Z., Guo, F., Sun, T., Xiao, Z., & Zhang, B. (2024). Not yet: Large language models cannot replace human respondents for psychometric research. OSF Preprints. https://doi.org/10.31219/osf.io/rwy9b |
| [56] | Wang, S., Li, R., Chen, X., Yuan, Y., Yang, M., & Wong, D. F. (2025). Exploring the impact of personality traits on LLM bias and toxicity. In C. Christodoulopoulos, T. Chakraborty, C. Rose, & V. Peng (Eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 4125-4143). Association for Computational Linguistics. |
| [57] | Wang, X., Duan, S., Yi, X., Yao, J., Zhou, S., Wei, Z.,... Xie, X. (2024). On the essence and prospect: An investigation of alignment approaches for big models. In K. Larson (Ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 8308-8316). Curran Associates Inc. |
| [58] |
Wu, M. S., & Peng, K. (2025). Human advantages and psychological transformations in the era of artificial intelligence. Acta Psychologica Sinica, 57(11), 1879-1884.
doi: 10.3724/SP.J.1041.2025.1879 |
| [59] | Xu, Y. (2024). Personality psychology (3rd ed.). Beijing, China: Beijing Normal University Publishing Group. |
| [60] |
Xu, Z., Sengar, N., Chen, T., Chung, H., & Oviedo- Trespalacios, O. (2025). Where is morality on wheels? Decoding large language model (LLM)-driven decision in the ethical dilemmas of autonomous vehicles. Travel Behaviour and Society, 40, 101039.
doi: 10.1016/j.tbs.2025.101039 URL |
| [61] | Yao, J., Yi, X., Wang, X., Wang, J., & Xie, X. (2023). From instructions to intrinsic human values--A survey of alignment goals for big models. arXiv. https://doi.org/10.48550/arXiv.2308.12014 |
| [62] | Yu, B., & Kim, J. (2024). Personality of AI. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing: 23rd International Conference (pp. 244-252). Springer, Cham. |
| [63] | Yuan, X., Hu, J., & Zhang, Q. (2024). A comparative analysis of cultural alignment in large language models in bilingual contexts. OSF Preprints. https://doi.org/10.31219/osf.io/6hpcf |
| [64] |
Zaim bin Ahmad, M. S., & Takemoto, K. (2025). Large-scale moral machine experiment on large language models. PloS One, 20(5), e0322776.
doi: 10.1371/journal.pone.0322776 URL |
| [65] | Zhao, G. L. (2023-06-20). Actual scores surpass ChatGPT! Baidu Wenxin Large Model Version 3.5 internal test application. China Science Daily. https://news.sciencenet.cn/htmlnews/2023/6/503256.shtm |
| [66] | Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y.,... Levy, O. (2023). LIMA:less is more for alignment. In A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), Proceedings of the 37th International Conference on Neural Information Processing Systems (pp. 55006-55021). Curran Associates Inc. |
| [67] |
Zhou, X., & Liu, H. (2024). New ethical challenges in the digital and intelligent era. Acta Psychologica Sinica, 56(2), 143-145.
doi: 10.3724/SP.J.1041.2024.00143 URL |
| Viewed | ||||||
|
Full text |
|
|||||
|
Abstract |
|
|||||