Psychological Steering of Large Language Models

TL;DR

This paper introduces a psychological steering framework for large language models that uses calibrated, semantic units for more effective personality trait manipulation, outperforming existing prompting methods.

Contribution

It presents a novel unbounded, fluency-constrained injection method based on psychological artifacts, improving personality steering in LLMs over prior approaches.

Findings

MD injections outperform P$^2$ in 11 of 14 LLMs with 3.6-16.4% gains.

Hybrid P$^2$ and MD injections outperform both in 13 of 14 LLMs with up to 26.7% gains.

MD injections align with the Linear Representation Hypothesis but show trait covariance patterns that differ from human psychology.

Abstract

Large language models (LLMs) emulate a consistent human-like behavior that can be shaped through activation-level interventions. This paradigm is converging on additive residual-stream injections, which rely on injection-strength sweeps to approximate optimal intervention settings. However, existing methods restrict the search space and sweep in uncalibrated activation-space units, potentially missing optimal intervention conditions. Thus, we introduce a psychological steering framework that performs unbounded, fluency-constrained sweeps in semantically calibrated units. Our method derives and calibrates residual-stream injections using psychological artifacts, and we use the IPIP-NEO-120, which measures the OCEAN personality model, to compare six injection methods. We find that mean-difference (MD) injections outperform Personality Prompting (P$^2$), an established baseline for OCEAN steering, in open-ended generation in 11 of 14 LLMs, with gains of 3.6\% to 16.4\%, overturning prior reports favoring prompting and positioning representation engineering as a new frontier in open-ended psychological steering. Further, we find that a hybrid of P$^2$ and MD injections outperforms both methods in 13 of 14 LLMs, with gains over P$^2$ ranging from 5.6\% to 21.9\% and from 3.3\% to 26.7\% over MD injections. Finally, we show that MD injections align with the Linear Representation Hypothesis and provide reliable, approximately linear control knobs for psychological steering. Nevertheless, they also induce OCEAN trait covariance patterns that depart from the Big Two model, suggesting a gap between learned representations and human psychology.