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Evaluating LLMs on Real-World Forecasting Against Expert Forecasters

Created by
  • Haebom

Author

Janna Lu

Outline

This paper evaluates the predictive power of state-of-the-art large-scale language models (LLMs). Using 464 prediction questions from Metaculus, we compared the performance of LLMs with that of leading predictors and expert groups. The results show that while state-of-the-art models achieve better Brier scores than human groups, they still lag significantly behind expert groups. While LLMs were not able to approach human accuracy until last year, recent models demonstrate significant progress.

Takeaways, Limitations

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Takeaways:
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Cutting-edge LLMs demonstrate significant advances in predictive capabilities.
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LLM's predictive performance can surpass that of human populations, but still falls short of that of expert populations.
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Further research is needed to improve the predictive power of LLM.
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Limitations:
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Limitations of the dataset used in the study raise questions about generalizability.
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There is a lack of root cause analysis for performance differences with the expert group.
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Further research is needed on the predictive reliability and interpretability of LLM.
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