Deep Persona: A Psychologically Grounded Architecture and Evaluation Framework for Role-Playing Agents and Simulations
Paper proposes Deep Persona, a three-layer psychologically grounded architecture for LLM role-playing agents plus a reference-free evaluation framework.
Deep Persona organizes personas into hierarchical levels of observable expression, latent beliefs, and core motivational drives, using scripted determinism and bounded agency to keep the model a reactive engine. The authors introduce a reference-free evaluation framework that benchmarks dialogue naturalness against empirical human distributions using clinical psychological instruments and adversarial stress-tests. Findings show LLMs achieve high pragmatic fluency but systematic weaknesses in emotional expression and joint attention; structured personas better align with human conversational behavior.
- Three-layer persona architecture: expression, latent beliefs, motivational drives
- Scripted determinism and bounded agency constrain the role-playing model
- Reference-free evaluation via clinical instruments and adversarial stress-tests
- LLMs fluent pragmatically but weak in emotional expression and joint attention
From the original article
1 min read at huggingface.co
Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions.
By Rotem Dror, Zohar Elyoseph, Yuval Haber · huggingface.co
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