Where AI power
meets human truth.

From Cornell University: LLMs, when aggregated, perform comparably to human crowd forecasts, and their accuracy improves further when informed by human predictions.

From Harvard Business School: How LLMs, can help researchers understand consumer preferences by generating multiple responses to survey questions. The study demonstrates that AI responses can produce realistic estimates of willingness-to-pay and proposes a fine-tuning method to enhance the alignment of AI's predictions with human responses in market research contexts.

From Harvard Kennedy School: AI-assisted polling is emerging as a potential enhancement to traditional methods by leveraging LLMs to simulate survey respondents and predict political opinions across diverse demographics.

From Natural Language Processing Journal: LLMs can enhance survey research by addressing issues like question-wording and response bias. With a careful, measured approach, LLMs can be used effectively and ethically while mitigating weaknesses.