Construction and Analysis of a Five-Factor Personality Assessment Model for Large Language Models (LLMs)

This project develops a multidimensional assessment framework for studying AI agent personality traits in large language models. Based on the Big Five model and personality-oriented prompts inspired by psychological scales such as NEO-PI-R, it quantitatively compares behavioral differences among models including DeepSeek-V3 and Qwen 2.5.

The analysis uses Python to process model-generated text and statistical methods, including correlation analysis, to compare model behavior across personality dimensions. The project provides benchmark-style results for future research on AI agent behavioral consistency and personality modeling.