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Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting

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arXiv:2605. 27668v1 Announce Type: new Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events.

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Authors: Hui Dai, Ryan Teehan, Parsa Torabian, Mengye Ren
Released: 2026-05-28 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Aligning LLMs with Human Uncertainty: A Beta-Bernoulli Calibrator for LLM Forecasting overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, good, alpha, rate, model. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.27668

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