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Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models

Paper recorded by Signals 4 on 2026-09-21 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-21 on arXiv · recorded by Signals 4 on 2026-09-22

Category: cs.AI · 人工智能 · first seen 2026-09-22

Abstract

In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates for their predictions. Existing approaches to uncertainty estimation for LLMs require access to log-probabilities output by the model or require fine-tuning access. However, many industrial LLM products use closed-source API models, and many such

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#17 most recent of 340 cs.AI papers we have recorded · ↑ newer: A Global Comparison of Schemas, Transparency, and Interoperability in · ↓ older: Partner-Specific Affective Precision in Social Active Inference
Cite this page: Pinocchio: Fast Uncertainty Estimates for Black-Box Language Models: the #17 most recent of 340 cs.AI papers we have recorded (as of 2026-09-21). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/pinocchio-fast-uncertainty-estimates-for-black-box-language-models.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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