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This paper introduces a mechanistic interpretability approach to steer LLM personality traits by identifying and intervening on latent features using sparse autoencoders, achieving controllable personality modulation while maintaining language performance.
This paper investigates whether fine-tuning LLMs on long-form essays with associated Big Five personality profiles stabilizes questionnaire responses and can induce target profiles, finding that while variance reduces, accuracy on the full five-dimensional profile remains near chance.