InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation Paper: bit.ly/3P4cQj9 How well can LLMs simulate real people? Most evaluations rely on surveys or questionnaires as proxies — never checking against what individuals actually said. 🔍 InterviewSim introduces an interview-grounded evaluation framework at scale: 671K+ Q&A pairs extracted from 23K verified interview transcripts across 1,000 public personalities, averaging 11.5 hours of content each. 📊 The framework evaluates simulation fidelity across four dimensions: → Content similarity → Factual consistency → Personality alignment (Big Five) → Factual knowledge retention (MCQ) 📌 Key finding: grounding in real interview data substantially outperforms biographical profiles or parametric knowledge alone. But how that data is used matters — retrieval-augmented methods capture personality style best, while chronological methods better preserve factual consistency and knowledge retention. 💡 The work also reveals that question type is a stronger predictor of difficulty than method choice. Social identity questions (birth dates, family details) yield the highest contradiction rates across all methods, while motivations and values questions are most forgiving. Authors: Yu Li @yooli23, Pranav Narayanan Venkit @PranavVenkit, Yada Pruksachatkun @yadapruksachatk, Chien-Sheng Wu @jasonwu0731 #FutureOfAI #EnterpriseAI #NLProc

Demographics aren't enough to simulate human behavior. 🧠 New research introduces SCOPE: a framework and persona dataset collection that moves beyond demographic templates to build richer AI personas grounded in sociopsychological structure. 📄 Paper: bit.ly/49ZduoK Key findings across 7 models: Demographics alone explain only ~1.5% of variance in human responses | Adding traits, values & identity narratives improves behavioral alignment while reducing bias | SCOPE personas outperform existing approaches on SimBench, an external social and behavioural benchmark. The work also shows SCOPE can augment existing persona systems like NVIDIA Nemotron to improve performance. ✨

Our key finding is that existing persona frameworks are often superficial or overly broad. A more socially sensitive approach produces personas that are less biased and more representative. Synthetic persona creation pipeline and corpus now available for research use. 🔓 Authors: Pranav Narayanan Venkit @PranavVenkit, Yu Li @yooli23, Yada Pruksachatkun @yadapruksachatk, Chien-Sheng Wu @jasonwu0731 #FutureOfAI #EnterpriseAI #AIResearch #AgenticAI #ResponsibleAI