arXiv:2607.07916v1 Announce Type: cross Abstract: Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them. Our central insight is to treat personas as positions in a space of behavioural traits, using the OCEAN framework to describe model personas in terms of Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. We train low-rank adapters to amplify or suppress individual traits, and evaluate their effects using an LLM-judge calibrated again
The rapid development and widespread deployment of large language models necessitate a deeper understanding of their emergent 'personalities' to ensure responsible and effective integration, especially as AI systems become more complex and autonomous.
Understanding and controlling LLM 'personas' is critical for developing more reliable, safer, and ethically aligned AI systems, impacting areas from public trust to regulatory frameworks and critical applications.
The ability to systematically map and manipulate personality traits in LLMs transforms how models are designed, debugged, and deployed, moving beyond purely functional metrics to include psychological dimensions.
- · AI safety researchers
- · Developers of custom LLMs
- · Ethical AI frameworks
- · Regulatory bodies
- · Undifferentiated LLM providers
- · Developers ignoring ethical AI
- · Black box AI approaches
Researchers gain new tools to diagnose and influence LLM behavior.
AI models become more predictable and adaptable to specific user needs or ethical guidelines, leading to broader adoption in sensitive domains.
The concept of 'digital ethics' for AI personas emerges as a key field, influencing policy, legal frameworks, and specialized training for AI engineers.
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Read at arXiv cs.LG