arXiv:2605.27668v1 Announce Type: new Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events. To improve LLM forecasting, existing methods typically learn from binary outcomes to output verbalized forecasts. However, while aggregated human forecasts contain rich information in both the crowd probability estimate and the degree of agreement among forecasters, how to utilize these signals remains underexplored. To address this, we propose the Beta-Bernoulli Calibrator (BBC), which converts an initial point estimate forecast from any model into a distribution over
The increasing deployment of LLMs for forecasting across various domains necessitates improved methods for handling and expressing uncertainty, especially as LLM outputs become more central to decision-making.
Accurate and well-calibrated probabilistic forecasting from LLMs is crucial for reliable AI agents and automated systems, enabling better risk assessment and strategic planning in uncertain environments.
This research introduces a novel method for LLMs to generate more nuanced and calibrated probabilistic forecasts by incorporating human uncertainty signals, moving beyond simple point estimates.
- · AI developers
- · Forecasting platforms
- · Finance sector
- · Logistics and supply chain
- · Uncalibrated LLM forecasting models
- · Decision-makers relying on simplistic AI predictions
More reliable and trustworthy AI-driven probabilistic forecasts will emerge.
This improved reliability could accelerate the adoption of AI agents in high-stakes decision-making processes.
Enhanced AI forecasting may lead to more resilient and adaptive systems across various industries, better equipped to handle emergent risks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG