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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23154 |
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Table of Contents:
- This study explores the potential of large language models (LLMs) to enhance expert forecasting through ensemble learning. Leveraging the European Central Bank's Survey of Professional Forecasters (SPF) dataset, we propose a comprehensive framework to evaluate LLM-driven ensemble predictions under varying conditions, including the intensity of expert disagreement, dynamics of herd behavior, and limitations in attention allocation.