# Personality Emulation Utilizing Large Language Models
## 2 Literature Review
### Personality Emulation and LLMs  
Although limited, existing research has investigated the capability of LLMs to emulate personality and explored methods to improve their ability. One study of the GPT-4, Llama 4, and Mistral LLM models found that each model had a unique Big Five personality [38]. The GPT-4 and Mistral models were found to rate higher in areas of agreeableness, conscientiousness, extraversion, and openness when compared to the Llama model [38]. Understanding the underlying personality of LLM models allows for a determination of what areas those models may excel in, as well as reveal the likeliness of bias occurring when attempting to mimic alternative personalities. An alternative study of the GPT-3.5 Turbo model explored it ability to shape to specific personality profiles and maintain consistent personality throughout interactions [39]. The study found that the model more consistently maintained a personality high in all Big Five traits [39]. However, when prompted to mimic a personality low in all Big Five traits, over time the model trended up across all traits [39]. Similar research has focused on tailoring LLM responses and personas to individual users [40; 41; 42]. A third study found that different LLM models have different Myers-Briggs Type Indicator (MBTI) scores [43]. Notable models studied included ChatGPT-3.5 (ENT)--extraverted, intuitive, thinking, judging), GPT-4 (INT)--introverted, intuitive, thinking, judging), and OpenLlama_v2 (INFI)--introverted, intuitive, feeling, judging) [43]. These studies demonstrate that different models display different personality traits, and that they are capable of emulating personality traits, particularly traits typically considered more social or extroverted.  
The work performed in this paper investigates the ability of LLMs to generate responses to email while emulating certain personality models. There is some similar existing research, but none specifically investigating the email use case. One similar study investigated the ability of LLMs to mimic personality while writing short stories [44]. This study found that there was overlap in linguistic behavior when comparing the GPT-3.5 and GPT-4 models investigated and human writing with the same Big Five personality [44]. The study also found that the accuracy of humans predicting personality trait levels varied across each trait and that knowledge of AI authorship decreased prediction accuracy [44]. In an investigation of the ability of LLMs to emulate personality for video game NPCs, researchers found that some LLM models were capable of aligning behaviors with psychometric values at up to 100% accuracy based on the International Personality Item Pool (IPIP) questionnaire [45]. In a study of open-source LLM models, researchers investigated the effect of different prompts on the LLMs' ability to mimic personality [46]. This study found that LLMs more consistently demonstrated the personality assigned to them when provided with a role and an associated personality when compared to only being assigned personality traits or types [46]. Existing research has shown that LLMs display some ability to emulate personality traits; however, the accuracy varies based on trait, model, prompt, and application.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Parameters** & **MMLU Pro [36]** & **HELM [37]** & **Writing Bench [35]** \\ \hline GPT-4.5 & N/A & 0.861 & Uncored & Uncored \\ GPT-4o & N/A & 87.2 & 0.779 & 8.16 \\ DeepSeek R1 & 671B & 0.84 & Uncored & 8.55 \\ Gemma 3 & 27B & 0.675 & Uncored & Uncored \\ Hermes 3 & 405B & Uncored & Uncored & Uncored \\ Llama 4 & 400B & 0.6592 & 0.812 & 7.01 \\ Claude 3 Opus & N/A & 0.6845 & 0.683 & Uncored \\ \hline \end{tabular}
\end{table}
Table 1: Overview of LLM models.