LLMs conditioned on actual psychometric profiles produce life stories from which independent LLMs recover personality scores at mean r=0.75, 85% of human reliability, with emotional patterns replicating in real human data.
IEEE Transactions on Computational Social Systems, 11(3):3362–3375
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Interactive evaluation of AI must be reframed as a distinct paradigm that maps interaction trajectories to judgments on process, recoverability, coordination, robustness, and system performance, supported by a two-axis taxonomy and design principles.
Persona-E² is a human-annotated dataset linking MBTI and Big Five personality traits to reader emotional responses across text domains, showing that personality data helps LLMs avoid surface-level stereotypes in emotion prediction.
citing papers explorer
-
Stories of Your Life as Others: A Round-Trip Evaluation of LLM-Generated Life Stories Conditioned on Rich Psychometric Profiles
LLMs conditioned on actual psychometric profiles produce life stories from which independent LLMs recover personality scores at mean r=0.75, 85% of human reliability, with emotional patterns replicating in real human data.
-
Interactive Evaluation Requires a Design Science
Interactive evaluation of AI must be reframed as a distinct paradigm that maps interaction trajectories to judgments on process, recoverability, coordination, robustness, and system performance, supported by a two-axis taxonomy and design principles.
-
Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events
Persona-E² is a human-annotated dataset linking MBTI and Big Five personality traits to reader emotional responses across text domains, showing that personality data helps LLMs avoid surface-level stereotypes in emotion prediction.