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arXiv AI By Yufei Chen, Yiran Zhao, Xiaogang Xu, Qipeng Xie, Jiafei Wu, Zhe Liu

LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting

Read the original on arXiv AI →

LEAP (Likelihood Elicitation and Aggregation for Probabilistic forecasting) is a new approach that reorganizes how evidence is used in LLM-based forecasting systems. Instead of a monolithic prediction that aggregates all evidence at once, LEAP examines each evidence item separately, elicits likelihood parameters, and combines them with an explicit prior to produce a posterior distribution. The method supports continuous, single-choice, and multi-choice forecasts and has been shown to improve prediction and calibration metrics across models on a benchmark covering forecasting, information-seeking, and browsing tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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