Psychological Concept Neurons: Can Neural Control Bias Probing and Shift Generation in LLMs?

TL;DR

This study investigates how Big Five personality traits are internally represented in LLMs and demonstrates that targeted neuron interventions can causally influence these internal representations and, to some extent, the generated outputs.

Contribution

The paper identifies the localization of Big Five trait representations in LLMs and shows that neuron-level interventions can bias internal traits and influence generated text.

Findings

Big Five information is decodable in early layers and persists through final layers.

Neurons selective to each Big Five trait are most common in mid layers.

Interventions on these neurons can reliably shift internal trait representations.

Abstract

Using psychological constructs such as the Big Five, large language models (LLMs) can imitate specific personality profiles and predict a user's personality. While LLMs can exhibit behaviors consistent with these constructs, it remains unclear where and how they are represented inside the model and how they relate to behavioral outputs. To address this gap, we focus on questionnaire-operationalized Big Five concepts, analyze the formation and localization of their internal representations, and use interventions to examine how these representations relate to behavioral outputs. In our experiment, we first use probing to examine where Big Five information emerges across model depth. We then identify neurons that respond selectively to each Big Five concept and test whether enhancing or suppressing their activations can bias latent representations and label generation in intended directions. We find that Big Five information becomes rapidly decodable in early layers and remains detectable through the final layers, while concept-selective neurons are most prevalent in mid layers and exhibit limited overlap across domains. Interventions on these neurons consistently shift probe readouts toward targeted concepts, with targeted success rates exceeding 0.8 for some concepts, indicating that the model's internal separation of Big Five personality traits can be causally steered. At the label-generation level, the same interventions often bias generated label distributions in the intended directions, but the effects are weaker, more concept-dependent, and often accompanied by cross-trait spillover, indicating that comparable control over generated labels is difficult even with interventions on a large fraction of concept-selective neurons. Overall, our findings reveal a gap between representational control and behavioral control in LLMs.