AINeutralarXiv – CS AI · 7h ago6/10
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Distribution Shift Alignment Helps LLMs Simulate Survey Response Distributions
Researchers introduced Distribution Shift Alignment (DSA), a novel fine-tuning method that enables large language models to more accurately simulate human survey responses by learning distribution patterns rather than memorizing training data. DSA outperforms existing methods across five public datasets and reduces required real-world data by 53-69%, offering significant cost savings for large-scale survey research.