# Investigating Large Language Models in Inferring Personality Traits from User Conversations

arXiv: https://arxiv.org/abs/2501.07532 · HTML: https://mlsanity.com/paper/2501.07532/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Predicting the Big Five Personality Traits in Chinese Counselling Dialogues Using Large Language Models](https://mlsanity.com/paper/2406.17287/) (2024)
- [DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markers](https://mlsanity.com/paper/2308.10758/) (2023)

## Built on by (most cited first, 5 of 5)

- [Personalized Text Generation with Contrastive Activation Steering](https://mlsanity.com/paper/2503.05213/) (2025)
- [Can LLM Agents Maintain a Persona in Discourse?](https://mlsanity.com/paper/2502.11843/) (2025)
- [EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling](https://mlsanity.com/paper/2509.02450/) (2025)
- [LLM Agent-Based Simulation of Student Activities and Mental Health Using Smartphone Sensing Data](https://mlsanity.com/paper/2508.02679/) (2025)
- [Mind Reading or Misreading? LLMs on the Big Five Personality Test](https://mlsanity.com/paper/2511.23101/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are demonstrating remarkable human like capabilities across diverse domains, including psychological assessment. This study evaluates whether LLMs, specifically GPT-4o and GPT-4o mini, can infer Big Five personality traits and generate Big Five Inventory-10 (BFI-10) item scores from user conversations under zero-shot prompting conditions. Our findings reveal that incorporating an intermediate step--prompting for BFI-10 item scores before calculating traits--enhances accuracy and aligns more closely with the gold standard than direct trait inference. This structured approach underscores the importance of leveraging psychological frameworks in improving predictive precision. Additionally, a group comparison based on depressive symptom presence revealed differential model performance. Participants were categorized into two groups: those experiencing at least one depressive symptom and those without symptoms. GPT-4o mini demonstrated heightened sensitivity to depression-related shifts in traits such as Neuroticism and Conscientiousness within the symptom-present group, whereas GPT-4o exhibited strengths in nuanced interpretation across groups. These findings underscore the potential of LLMs to analyze real-world psychological data effectively, offering a valuable foundation for interdisciplinary research at the intersection of artificial intelligence and psychology.