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

Research Questions

  1. Can an LLM-based model learn and imitate an individual’s thoughts and judgments from their conversations?
  2. Can such individual-centered models be used in survey research?
  3. Is it feasible to simulate personalized opinions in a practical single-device environment (e.g., a 40GB GPU)?

Results

  • The Doppelgänger model replicated individual opinions with high accuracy.
  • Achieved 67% accuracy on a 5-point scale and 80% accuracy on a 3-point scale.
  • Longer context windows (e.g., 3,000 tokens) and more training epochs increased accuracy.
  • Outperformed commercial models such as GPT-3.5 Turbo and Gemini.
  • Proven feasible on a single 40GB GPU.

Findings

  • The Doppelgänger model can reproduce opinions not only at the group level but also at the individual level with strong fidelity.

  • Surveyed LLMs (0.46 / 0.65 accuracy) and commercial models showed insufficient performance in individualized imitation.

  • Predictions based solely on metadata were weak and biased, underscoring the importance of conversational data.

  • The model learned effectively even with an average of 21 conversation samples per individual.

  • The approach provides a powerful tool for capturing individual-level heterogeneity and generating personalized responses.

  • LLM Models: 5

  • Synthetic Data: 5

  • Method: 5

  • Speed: 4

  • Ethics: 2

  • Accuracy: 5

  • Demographics: 2

If you would like to access more detailed information about this article, click here to view the supplementary material.

5 min read

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