UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

👽 Out-of-Distribution Detection with PyTorch

Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores

A list of papers that studies out-of-distribution (OOD) detection and misclassification detection (MisD)

📈 SiRE (Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data), accepted by CIKM'2022 🗽

PyTorch implementation of our ECCV 2022 paper "Rethinking Confidence Calibration for Failure Prediction"

Learning from scratch a confidence measure

Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks https://arxiv.org/abs/1910.11933 or https://ieeexplore.ieee.org/document/9053264

Official pytorch implementation of the paper [Adaptive confidence thresholding for monocular depth estimation]

Demo code for GACE: Geometry Aware Confidence Enhancement

Free WordPress Plugin: This sample size calculator enables you to calculate the minimum sample size and the margin of error. Learn about sample size, the margin of error, & confidence interval. www.calculator.io/sample-size-calculator/

This repo contains code to perform Bootstrap Confidence Intervals estimation (a.k.a. Monte Carlo Confidence Interval or Empirical Confidence Interval estimation) for Machine Learing models.

[ACL 2025] Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?.

Benchmark for "Offline Policy Comparison with Confidence"

KBS 2024 Paper, A Confidence-based Knowledge Integration Framework for Cross-Domain Table Question Answering

Project of ACL 2025 MlingConf: A Comprehensive Investigation of Multilingual Confidence Estimation for Large Language Models

Source code for predicting confidence scores for the samples in t-sne embeddings.

Code for "Confidence-Driven Hierarchical Classification of Cultivated Plant Stresses"

number of times an experiment should be repeated for a 95% probability