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Wisdom of the silicon crowd: LLM ensemble prediction capabilities rival human crowd accuracy
16 references·1 citing·55 second hop
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References

  • 2007Strictly Proper Scoring Rules, Prediction, and Estimation5,771 cited
  • 2023Biases in Large Language Models: Origins, Inventory, and Discussion364 cited
  • 2014Identifying Expertise to Extract the Wisdom of Crowds363 cited
  • 2012When Are Two Heads Better than One and Why?256 cited
  • 2014When is a crowd wise?184 cited
  • 2019Harnessing the Wisdom of Crowds183 cited
  • 2012A continuum of learning: from rote memorization to meaningful learning in organic chemistry178 cited
  • 2023Which Humans?151 cited
  • 2023Large language models show human-like content biases in transmission chain experiments137 cited
  • 2014Two Reasons to Make Aggregated Probability Forecasts More Extreme130 cited
  • 2014Accuracy of forecasts in strategic intelligence101 cited
  • 2014Forecasting Tournaments98 cited
  • 2007The acquiescence effect in responding to a questionnaire.91 cited
  • 2024Diminished diversity-of-thought in a standard large language model83 cited
  • 2013The Wisdom of Competitive Crowds65 cited
  • 2024Cognitive Bias in Decision-Making with LLMs

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  • 2025Making Accurate Judgements in Child Welfare: Comparing ChatGPT With Qualified Social Workers5 cited