Localizing Persona Representations in LLMs
This work addresses the problem of understanding internal representations in LLMs for researchers and developers, but it is incremental as it builds on existing methods to analyze known phenomena.
The study investigated how personas, defined by human characteristics and beliefs, are encoded in the representation space of large language models (LLMs), finding that significant differences occur only in the final third of decoder layers, with overlapping activations for ethical perspectives like moral nihilism and utilitarianism but distinct regions for political ideologies such as conservatism and liberalism.
We present a study on how and where personas -- defined by distinct sets of human characteristics, values, and beliefs -- are encoded in the representation space of large language models (LLMs). Using a range of dimension reduction and pattern recognition methods, we first identify the model layers that show the greatest divergence in encoding these representations. We then analyze the activations within a selected layer to examine how specific personas are encoded relative to others, including their shared and distinct embedding spaces. We find that, across multiple pre-trained decoder-only LLMs, the analyzed personas show large differences in representation space only within the final third of the decoder layers. We observe overlapping activations for specific ethical perspectives -- such as moral nihilism and utilitarianism -- suggesting a degree of polysemy. In contrast, political ideologies like conservatism and liberalism appear to be represented in more distinct regions. These findings help to improve our understanding of how LLMs internally represent information and can inform future efforts in refining the modulation of specific human traits in LLM outputs. Warning: This paper includes potentially offensive sample statements.