The field of large language models (LLMs) has grown rapidly in recent years, driven by the desire for better efficiency, interpretability, and safe use. Building on the novel approach of "activation engineering," this study explores personality modification in LLMs, drawing inspiration from research like Refusal in LLMs Is Mediated by a Single Direction (arXiv:2406.11717) and Steering Llama 2 via Contrastive Activation Addition (arXiv:2312.06681). We leverage activation engineering to develop a method for identifying and adjusting activation directions related to personality traits, which may allow for dynamic LLM personality fine-tuning. This work aims to further our understanding of LLM interpretability while examining the ethical implications of such developments.
@article{arxiv.2412.10427,
title = {Identifying and Manipulating Personality Traits in LLMs Through Activation Engineering},
author = {Rumi Allbert and James K. Wiles and Vlad Grankovsky},
journal= {arXiv preprint arXiv:2412.10427},
year = {2025}
}