InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation
#InterviewSim #Personality Simulation #Large Language Models #Evaluation Framework #Interview Data #AI Research #arXiv #Computational Linguistics
📌 Key Takeaways
- Researchers developed InterviewSim, a framework for personality simulation using real interview data
- The study extracted 671,000 Q&A pairs from 23,000 interviews of 1,000 public personalities
- Four complementary metrics evaluate content similarity, factual consistency, personality alignment, and knowledge retention
- Methods using real interview data outperform those using biographical profiles or parametric knowledge
📖 Full Retelling
Researchers Yu Li, Pranav Narayanan Venkit, Yada Pruksachatkun, and Chien-Sheng Wu introduced InterviewSim, a scalable framework for interview-grounded personality simulation, in a paper submitted to arXiv on February 23, 2026, addressing the gap in authentic personality assessment by evaluating large language models against actual interview data rather than relying on proxies like surveys or questionnaires. The researchers extracted over 671,000 question-answer pairs from 23,000 verified interview transcripts across 1,000 public personalities, with each personality having an average of 11.5 hours of interview content available for analysis. This comprehensive dataset provides a more authentic foundation for personality simulation than previous approaches that used demographic surveys or short AI-led interviews as proxies. The team proposes a multi-dimensional evaluation framework with four complementary metrics measuring content similarity, factual consistency, personality alignment, and factual knowledge retention, enabling more accurate assessment of how well large language models can simulate human personalities. Through systematic comparison, the researchers demonstrated that methods grounded in real interview data substantially outperform those relying solely on biographical profiles or the model's parametric knowledge, revealing important insights about how interview data should be utilized for optimal results.
🏷️ Themes
Artificial Intelligence, Personality Simulation, Evaluation Frameworks, Natural Language Processing
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Entity Intersection Graph
Connections for Large language model:
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Educational technology
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Reinforcement learning
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Machine learning
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Artificial intelligence
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Benchmark
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Original Source
--> Computer Science > Computation and Language arXiv:2602.20294 [Submitted on 23 Feb 2026] Title: InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation Authors: Yu Li , Pranav Narayanan Venkit , Yada Pruksachatkun , Chien-Sheng Wu View a PDF of the paper titled InterviewSim: A Scalable Framework for Interview-Grounded Personality Simulation, by Yu Li and 2 other authors View PDF HTML Abstract: Simulating real personalities with large language models requires grounding generation in authentic personal data. Existing evaluation approaches rely on demographic surveys, personality questionnaires, or short AI-led interviews as proxies, but lack direct assessment against what individuals actually said. We address this gap with an interview-grounded evaluation framework for personality simulation at a large scale. We extract over 671,000 question-answer pairs from 23,000 verified interview transcripts across 1,000 public personalities, each with an average of 11.5 hours of interview content. We propose a multi-dimensional evaluation framework with four complementary metrics measuring content similarity, factual consistency, personality alignment, and factual knowledge retention. Through systematic comparison, we demonstrate that methods grounded in real interview data substantially outperform those relying solely on biographical profiles or the model's parametric knowledge. We further reveal a trade-off in how interview data is best utilized: retrieval-augmented methods excel at capturing personality style and response quality, while chronological-based methods better preserve factual consistency and knowledge retention. Our evaluation framework enables principled method selection based on application requirements, and our empirical findings provide actionable insights for advancing personality simulation research. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2602.2029...
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