Your Language Model Secretly Contains Personality Subnetworks

TL;DR

This paper reveals that large language models inherently contain distinct persona-specific subnetworks within their parameters, enabling behavior adaptation without external prompts or fine-tuning, thus offering a new perspective on model personalization.

Contribution

The authors introduce a training-free method to identify and isolate persona subnetworks in LLMs, demonstrating their effectiveness in enhancing persona alignment and interpretability.

Findings

Persona subnetworks are embedded in LLM parameters.

Contrastive pruning improves separation of opposing personas.

Method outperforms baselines requiring external knowledge.

Abstract

Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.