arXiv:2604.24079v2 Announce Type: replace Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on surface-level lexical or stylistic cues, treating dialogue as a flat sequence of tokens and failing to capture the deeper discourse-level structures that sustain persona consistency. To address this limitation, we propose a novel analytical framework that interprets LLM dialogue through bridging inference -- implicit conceptual relations that connect utterances via shared world knowledge and disco
The rapid advancement and deployment of Large Language Models necessitate more sophisticated methods for understanding and controlling their behavior, particularly their emergent personas.
Understanding and engineering LLM personas through deeper discourse analysis is crucial for developing more reliable, controllable, and contextually appropriate AI systems across various applications.
The focus in LLM persona discovery shifts from surface-level linguistic cues to deeper, inference-based conceptual relations, enabling more robust and consistent persona generation and analysis.
- · AI developers
- · LLM researchers
- · Ethical AI organizations
- · Custom AI platform providers
- · Developers relying on superficial persona engineering
- · Applications needing highly stable, specific personas
- · Generative AI platforms with limited persona control
Improved understanding and control over intrinsic LLM behavior leads to more predictable and safer AI interactions.
Enhanced persona engineering enables development of more specialized and trustworthy AI agents for sensitive tasks and customer-facing roles.
The ability to consistently imbue LLMs with specific, complex personas could redefine human-AI collaboration and the scope of autonomous AI applications.
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Read at arXiv cs.CL