  We test the abilities of specialised deep neural networks like PersonalityMap
as well as general LLMs like GPT-4o and Claude 3 Opus in understanding human
personality. Specifically, we compare their ability to predict correlations
between personality items to the abilities of lay people and academic experts.
We find that when compared with individual humans, all AI models make better
predictions than the vast majority of lay people and academic experts. However,
when selecting the median prediction for each item, we find a different
pattern: Experts and PersonalityMap outperform LLMs and lay people on most
measures. Our results suggest that while frontier LLMs' are better than most
individual humans at predicting correlations between personality items,
specialised models like PersonalityMap continue to match or exceed expert human
performance even on some outcome measures where LLMs underperform. This
provides evidence both in favour of the general capabilities of large language
models and in favour of the continued place for specialised models trained and
deployed for specific domains.
