arXiv:2607.02368v1 Announce Type: cross Abstract: Evaluations of LLM personas via psychometric questionnaires typically rely on aggregate scores, discarding within-instance correlation structure. We test whether this geometric structure is intrinsic or frame-dependent. Constructing within-instance correlation matrices from IPIP-50 responses, we analyze geometry on SPD manifolds under manipulated question orderings in GPT-4o simulating American and Chinese-American personas. We find that persona expression comprises two dissociable components: aggregated features (Big Five scores) degrade under
The proliferation of advanced LLMs like GPT-4o and the increasing focus on AI alignment and ethical AI development necessitate deeper understanding of their 'persona' and potential biases.
Understanding the 'dual nature' of LLM persona and its frame-dependence is crucial for developing robust, fair, and culturally aware AI, impacting deployment strategies and regulatory frameworks.
This research reveals that LLM personas are not monolithic, but are influenced by both aggregate tendencies and specific contextual framing, suggesting more nuanced control mechanisms are possible.
- · AI ethicists
- · LLM developers
- · AI governance researchers
- · Developers of simplistic LLM evaluations
- · Users unaware of LLM persona malleability
Improved methods for evaluating and fine-tuning LLM behavior will emerge, enabling more consistent and predictable AI interactions.
The ability to manipulate LLM personas through frame-dependent geometry could lead to highly specialized and culturally nuanced AI applications, but also potential misuse.
This deeper understanding of AI 'identity' might influence philosophical debates on AI consciousness and rights, though this remains highly speculative.
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Read at arXiv cs.LG