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This research aims to improve GDP forecasting by utilizing unstructured textual data through large language models.

Large language models for unstructured economic data processing: Enhancing GDP forecasting accuracy with heterogeneous textual information

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This research aims to improve GDP forecasting by utilizing unstructured textual data through large language models.
Developed a hybrid framework integrating large language models with traditional economic indicators.
Evaluated model performance in a pseudo real-time environment using four datasets.
Compared LLM-augmented models against structured-data benchmarks.
Achieved relative RMSE reductions of up to 29% compared to traditional models.
Significant improvements noted particularly during economic recessions.
Demonstrated that unstructured text provides critical insights for economic turning points.

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Randomized trial enhances GDP forecasting accuracy using unstructured textual information, suggesting improved economic insights.

Qiu et al. (2026) studied this question.

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