Adopting personas shifts word meanings in large language models
Persona Perspectives: Adopting Viewpoints Reorganizes Representations in LLMs
Computation and Language
Summary
Large language models (LLMs) can take on different perspectives, called personas, that change how they understand words and ideas. The paper shows that when an LLM adopts a political persona, like liberal or conservative, the way it organizes meanings in its 'memory' changes systematically. This change affects how the model rates the truthfulness and morality of statements. The researchers warn that common methods used to monitor LLM behavior may give biased results if the model is adopting a persona.
What this means in practice
- •For ai safety teams: Identify how persona adoption in LLMs affects safety monitoring tools for truth and morality assessments in content moderation.
- •For chatbot developers: Adjust chatbot response models to accommodate shifts in word meaning when LLMs take on different personas for more consistent outputs.
Authors
Austin C. Kozlowski, Avital Mintz, Madison Lee
Abstract
Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features maintain a consistent representation across contexts. LLMs, however, can flexibly adopt different perspectives or "personas" that suggest different underlying systems of conceptual relations. In this paper, we examine whether prompting an LLM to adopt a persona alters the representational geometry of the model's latent space. We extract representations of over 9,000 common words and 33 semantic axes from models adopting liberal and conservative political personas and compare these against two neutral baselines. We find that persona conditioning systematically alters both the projections of words onto semantic axes and the alignments among the axes themselves in directions consistent with the specified ideology. We then test the implications of this reorganization for two safety-relevant features: truthfulness and morality. Linear probes, SAE-based classifiers, and classifiers built from manually selected word representations, all trained without a persona, produce divergent results when applied to models conditioned on political personas, producing politically slanted ratings of truth and morality on controversial statements. These findings suggest that internal representations, as they are conventionally extracted, can exhibit different geometric relations when the model adopts personas, potentially undermining monitoring efforts.