Scaling Personality Control in LLMs with Big Five Scaler Prompts

ACL ARR 2025 May Submission1374 Authors

Abstract: We present Big5-Scaler, a prompt-based framework for conditioning large language models (LLMs) with controllable Big Five personality traits. By embedding numeric trait values into natural language prompts, our method enables fine-grained personality control without additional training. We evaluate Big5-Scaler across trait expression, dialogue generation, and human trait imitation tasks. Results show that it induces consistent and distinguishable personality traits across models, with performance varying by prompt type and scale. Our analysis highlights the effectiveness of concise prompts and lower trait intensities, providing a efficient approach for building personality-aware dialogue agents

Paper Type: Long

Research Area: Special Theme (conference specific)

Research Area Keywords: LLM/AI agents, Human-AI interaction/cooperation, NLP and Psychology, Big5

Contribution Types: NLP engineering experiment, Reproduction study, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 1374