PersLLM introduces a parameter-efficient framework for personality detection from social media text, dramatically reducing computational costs while maintaining accuracy.

• Addresses the growing complexity and expense of fine-tuning large language models
• Enables effective personality profiling with significantly fewer parameters
• Provides reliable prediction outcomes with less computational overhead
• Creates new opportunities for security applications in user authentication and risk assessment

This research matters for security professionals by offering more accessible tools for behavior analysis and user profiling, potentially enhancing threat detection systems without requiring massive computational resources.

Less but Better: Parameter-Efficient Fine-Tuning of Large Language Models for Personality Detection