United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections

Understand

"Synthetic samples" based on large language models (LLMs) have been argued to serve as efficient alternatives to surveys of humans, assuming that their training data includes information on human attitudes and behavior.

• However, LLM-synthetic samples might exhibit bias, for example due to training data and fine-tuning processes being unrepresentative of diverse contexts.
• Such biases risk reinforcing existing biases in research, policymaking, and society.
• Therefore, researchers need to investigate if and under which conditions LLM-generated synthetic samples can be used for public opinion prediction.