arXiv:2606.11502v1 Announce Type: new Abstract: Language models can state that "the Earth orbits the Sun" and, when role-playing Aristotle, assert the opposite. Recent work argues that persona adoption is fundamental to how language models operate, with models constantly selecting the most appropriate persona for a given context. Does such role-playing merely change the model's outputs, or does it also affect what the model internally represents as truthful? We study this question with linear truth probes, applying them to LLMs role-playing historical personas whose likely beliefs differ from
The rapid advancement and widespread deployment of large language models are prompting deeper investigations into their internal mechanisms and reliability, particularly as their applications become more critical.
Understanding whether LLMs merely output plausible text or internally integrate beliefs during role-playing has profound implications for their trustworthiness, safety, and the development of truly intelligent systems.
This research shifts the understanding of LLM persona adoption from purely output-focused to potentially belief-state altering, suggesting a more complex internal representation than previously assumed by some.
- · AI Safety Researchers
- · LLM Developers (improving control)
- · Academia (cognitive science)
- · Developers relying on superficial persona adoption
- · Organizations deploying unchecked LLMs for sensitive tasks
This research provides a novel method for probing the internal representations of large language models during role-playing scenarios.
A deeper understanding of how persona influences model beliefs could lead to more robust and less exploitable AI systems, especially in applications requiring truthful or consistent responses under varying contexts.
If models genuinely alter internal 'beliefs' during role-play, it could open new avenues for developing AI that can dynamically adapt its underlying knowledge base based on context, moving closer to human-like understanding rather than mere pattern matching.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL