Mechanistic Personality Analysis of LLMs Steering Personality via Latent Feature Interventions
Quick Answer
This study introduces a mechanistic interpretability approach for Large Language Models (LLMs) that enhances OCEAN personality traits through latent feature interventions.
Quick Take
This study introduces a mechanistic interpretability approach for Large Language Models (LLMs) that enhances OCEAN personality traits through latent feature interventions. By using sparse autoencoders and contrastive activation analysis, the method applies targeted shifts in hidden states, achieving improved personality control while maintaining high performance on standard benchmarks.
Key Points
- Utilizes sparse autoencoders to identify latent directions for OCEAN traits.
- Implements an additive steering vector to enhance target traits in LLMs.
- Maintains overall language modeling performance during personality adjustments.
- Explores grid search optimization for balancing personality and task performance.
- Demonstrates potential for controllable personality steering in LLMs.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Large Language Models (LLMs) have demonstrated the ability to simulate human-like OCEAN personality traits in generated text. Previous efforts have focused on prompt engineering or fine-tuning to shape LLM personality. In this work, we propose a mechanistic interpretability approach that directly intervenes on the model's latent features. Our method identifies latent directions in the residual stream corresponding to a target OCEAN trait using sparse autoencoders (SAEs) and contrastive activation analysis. We formalize an additive steering vector in activation space and demonstrate how applying a small additive shift to the hidden states enhances the target trait while preserving overall language modeling performance. To determine the optimal combination of feature shifts, we explore a linear weighting heuristic with grid search optimization that balances personality expression with task performance. Our approach shows promise in controllably steering personality traits at the mechanistic level while maintaining high performance on standard benchmarks.
| Comments: | Written in 2024; submitted to arXiv 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.28770 [cs.AI] |
| (or arXiv:2606.28770v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28770 arXiv-issued DOI via DataCite |
Submission history
From: David Courtis [view email]
[v1]
Sat, 27 Jun 2026 06:53:51 UTC (1,153 KB)
— Originally published at arxiv.org
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