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Author

Serina Chang

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#artificial intelligence Review Sep 2026

ORQA: An Occupation-Realistic Question and Answer Framework for LLM Professional Knowledge

We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations via task definitions or utilize expert knowledge which is difficult to obtain at scale and expensive. ORQA complements both of these methods by connecting O*NET occupati...

Shreyas R. Krishnan, Serina Chang, Abhishek Nagaraj · 0 citations
#artificial intelligence Review Jul 2026

Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation

Anamnesis produces opinion distributions that more closely match real-world survey data than standard persona-prompting baselines, offering a transparent, reproducible, and open-source alternative to proprietary simulation services.

Song Yu, Joseph Suh, Serina Chang et al. · 0 citations

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