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...

Full description

Saved in:
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/
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items