This content originally appeared on HackerNoon and was authored by Precedent Publishing House
:::info 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).
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Table of Links
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
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.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Precedent Publishing House
Precedent Publishing House | Sciencx (2025-03-19T11:00:09+00:00) How Future Narratives Improve ChatGPT’s Oscars Predictions. Retrieved from https://www.scien.cx/2025/03/19/how-future-narratives-improve-chatgpts-oscars-predictions/
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