# Psychological Steering in LLMs: An Evaluation of Effectiveness and Trustworthiness

arXiv: https://arxiv.org/abs/2510.04484 · HTML: https://mlsanity.com/paper/2510.04484/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 8 of 8)

- [Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://mlsanity.com/paper/2601.14004/) (2026)
- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [Tracing Moral Foundations in Large Language Models](https://mlsanity.com/paper/2601.05437/) (2026)
- [Personality Expression Across Contexts: Linguistic and Behavioral Variation in LLM Agents](https://mlsanity.com/paper/2602.01063/) (2026)
- [EPSVec: Efficient and Private Synthetic Data Generation via Dataset Vectors](https://mlsanity.com/paper/2602.21218/) (2026)
- [Psychological Steering of Large Language Models](https://mlsanity.com/paper/2604.14463/) (2026)
- [ODRA: Synthesizing Cognitive Behavioral Therapy Sessions with Structured Chain-Of-Thought and Dynamic Patient Resistance](https://mlsanity.com/paper/2608.04524/) (2026)
- [LITERARYBIGFIVE: Author-Personalized Text Generation in a Unified Interpretable Space](https://mlsanity.com/paper/2608.23124/) (2026)

## Abstract (arXiv)

The ability to control LLMs' emulated emotional states and personality traits is an essential step in enabling rich, human-centered interactions in socially interactive settings. We introduce PsySET, a Psychologically-informed benchmark to evaluate LLM Steering Effectiveness and Trustworthiness across the emotion and personality domains. Our study spans four models from different LLM families paired with various steering strategies, including prompting, fine-tuning, and representation engineering. Our results indicate that prompting is consistently effective but limited in intensity control, whereas vector injections achieve finer controllability while slightly reducing output quality. Moreover, we explore the trustworthiness of steered LLMs by assessing safety, truthfulness, fairness, and ethics, highlighting potential side effects and behavioral shifts. Notably, we observe idiosyncratic effects; for instance, even a positive emotion like joy can degrade robustness to adversarial factuality, lower privacy awareness, and increase preferential bias. Meanwhile, anger predictably elevates toxicity yet strengthens leakage resistance. Our framework establishes the first holistic evaluation of emotion and personality steering, offering insights into its interpretability and reliability for socially interactive applications.