LLM-Based Doppelgänger Models: Leveraging Synthetic Data for Human-Like Responses in Survey Simulations

LLM-Based Doppelgänger Models: Leveraging Synthetic Data for Human-Like Responses in Survey Simulations

This study explores whether large language models (LLMs) can learn a person’s opinions from their speech and act based on that knowledge. It also proposes the potential for utilizing such trained models in survey research. Traditional survey research collects information through standardi...

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Bibliographic Details
Main Authors: Suhyun Cho, Jaeyun Kim, Jang Hyun Kim
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
LLM
survey research
NLP
NLU
synthetic data
Online Access:https://ieeexplore.ieee.org/document/10758652/
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