8 citations · 21 across the 5 of their papers we have counts for
5 papers
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy
Philipp Schoenegger, Indre Tuminauskaite, Peter S. Park +1
Human forecasting accuracy in practice relies on the 'wisdom of the crowd' effect, in which predictions about future events are significantly improved by aggregating across a crowd…
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
Philipp Schoenegger, Peter S. Park, Ezra Karger +2
Large language models (LLMs) match and sometimes exceeding human performance in many domains. This study explores the potential of LLMs to augment human judgement in a forecasting…
Large Language Model Prediction Capabilities: Evidence from a Real-World Forecasting Tournament
Philipp Schoenegger, Peter S. Park
Accurately predicting the future would be an important milestone in the capabilities of artificial intelligence. However, research on the ability of large language models to provid…
Effect of many-body quantum fluctuations on matrix Berry phases of a two-dimensional n-type semiconductor quantum dot
S. C. Kim, Y. J. Kim, P. S. Park +2
In the presence of spin-orbit coupling and inversion symmetry of the lateral confinement potential a single electron does not exhibit matrix Berry phases in quasi-two-dimensional s…