Beyond Raw Transcripts: Structured Persona Extraction for LLM-Based Digital Twins
LLM-based "digital twins" aim to simulate how an individual would behavein new environments or respond to novel questions, given some representation of that individual's prior responses. A common approach constructs this representation from survey transcripts or summaries responses. Prior work shows that compressing long transcripts into shorter LLM-generated summaries does not significantly reduce predictive accuracy, suggesting that information volume is not the primary bottleneck. In this work, we argue that the key limitation is instead structural:how persona information is organized before being provided to thesimulator model. We study this by comparing unstructured summaries with st...
| Authors | Iris Ye, Tianze Deng, Ozan Candogan |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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