BLF achieves state-of-the-art binary forecasting on ForecastBench by using linguistic belief states updated in tool-use loops, hierarchical multi-trial logit averaging, and hierarchical Platt scaling calibration.
Park, Ezra Karger, Sean Trott, and Philip E
2 Pith papers cite this work. Polarity classification is still indexing.
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Three independent LLMs exhibit correlated forecasting errors on 568 binary questions but human predictions show no activation of this shared bias.
citing papers explorer
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Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs
BLF achieves state-of-the-art binary forecasting on ForecastBench by using linguistic belief states updated in tool-use loops, hierarchical multi-trial logit averaging, and hierarchical Platt scaling calibration.
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The Oracle's Fingerprint: Correlated AI Forecasting Errors and the Limits of Bias Transmission
Three independent LLMs exhibit correlated forecasting errors on 568 binary questions but human predictions show no activation of this shared bias.