The study explores the use of language models for automated forecasting, comparing their performance to human forecasters. By collecting a dataset of real-world questions and fine-tuning LM systems, the authors show promising results in approaching human-level forecasting accuracy.
The authors highlight the importance of accurate forecasting for decision-making in various fields such as economics, geopolitics, and epidemiology. They propose a retrieval-augmented LM system that significantly improves upon baseline performance and approaches human crowd predictions on competitive platforms.
Through detailed data curation and optimization processes, the study showcases the potential of LMs in automating forecasting tasks. The system's components include retrieval, reasoning, and aggregation steps to generate accurate predictions at scale.
Overall, the research emphasizes the role of language models in providing timely and informed forecasts to support institutional decision-making across different domains.
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by Danny Halawi... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2402.18563.pdfDeeper Inquiries