Authors:
(1) Pham Hoang Van, Department of Economics, Baylor University Waco, TX, USA (Van Pham@baylor.edu);
(2) Scott Cunningham, Department of Economics, Baylor University Waco, TX, USA (Scott Cunningham@baylor.edu). Table of Links Abstract and 1 Introduction 2 Direct vs Narrative Prediction 3 Prompting Methodology and Data Collection 4 Results 4.1 Establishing the Training Data Limit with Falsifications 4.2 Results of the 2022 Academy Awards Forecasts 5 Predicting Macroeconomic Variables 5.1 Predicting Inflation with an Economics Professor 5.2 Predicting Inflation with a Jerome Powell, Fed Chair 5.3 Predicting Inflation with Jerome Powell and Prompting with Russia’s Invasion of Ukraine 5.4 Predicting Unemployment with an Economics Professor 6 Conjecture on ChatGPT-4’s Predictive Abilities in Narrative Form 7 Conclusion and Acknowledgments Appendix A. Distribution of Predicted Academy Award Winners B. Distribution of Predicted Macroeconomic Variables References A. Distribution of Predicted Academy Award Winners Detailed figures illustrating the distribution of predicted winners for each Academy Award category, using the four prompting styles, are provided here. These figures showcase the improved accuracy of GPT-4 in predicting winners when prompted with a future narrative. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Pham Hoang Van, Department of Economics, Baylor University Waco, TX, USA (Van Pham@baylor.edu); (2) Scott Cunningham, Department of Economics, Baylor University Waco, TX, USA (Scott Cunningham@baylor.edu). Authors: Authors: (1) Pham Hoang Van, Department of Economics, Baylor University Waco, TX, USA (Van Pham@baylor.edu); (2) Scott Cunningham, Department of Economics, Baylor University Waco, TX, USA (Scott Cunningham@baylor.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Direct vs Narrative Prediction 2 Direct vs Narrative Prediction 3 Prompting Methodology and Data Collection 3 Prompting Methodology and Data Collection 4 Results 4.1 Establishing the Training Data Limit with Falsifications 4.1 Establishing the Training Data Limit with Falsifications 4.2 Results of the 2022 Academy Awards Forecasts 4.2 Results of the 2022 Academy Awards Forecasts 5 Predicting Macroeconomic Variables 5 Predicting Macroeconomic Variables 5.1 Predicting Inflation with an Economics Professor 5.1 Predicting Inflation with an Economics Professor 5.2 Predicting Inflation with a Jerome Powell, Fed Chair 5.2 Predicting Inflation with a Jerome Powell, Fed Chair 5.3 Predicting Inflation with Jerome Powell and Prompting with Russia’s Invasion of Ukraine 5.3 Predicting Inflation with Jerome Powell and Prompting with Russia’s Invasion of Ukraine 5.4 Predicting Unemployment with an Economics Professor 5.4 Predicting Unemployment with an Economics Professor 6 Conjecture on ChatGPT-4’s Predictive Abilities in Narrative Form 6 Conjecture on ChatGPT-4’s Predictive Abilities in Narrative Form 7 Conclusion and Acknowledgments 7 Conclusion and Acknowledgments Appendix Appendix A. Distribution of Predicted Academy Award Winners A. Distribution of Predicted Academy Award Winners B. Distribution of Predicted Macroeconomic Variables B. Distribution of Predicted Macroeconomic Variables References References A. Distribution of Predicted Academy Award Winners Detailed figures illustrating the distribution of predicted winners for each Academy Award category, using the four prompting styles, are provided here. These figures showcase the improved accuracy of GPT-4 in predicting winners when prompted with a future narrative. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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How Future Narratives Improve ChatGPT’s Oscars Predictions

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