Search Results for author:
Found 7 papers, 5 papers with code
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.
Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification
Uncertainty scores leverage information encapsulated in the output of a neural network or its layers to detect unreliable predictions, and we show that they can be used to fact-check the atomic claims in the LLM output.
ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
To increase the accessibility of our model to non-English speakers, we propose to make use of cascaded machine translation and show that this approach can perform comparably to natively multilingual models while being easier to scale to a large number of languages.
How Certain is Your Transformer?
In this work, we consider the problem of uncertainty estimation for Transformer-based models.
Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampling
Uncertainty estimation for machine learning models is of high importance in many scenarios such as constructing the confidence intervals for model predictions and detection of out-of-distribution or adversarially generated points.
Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning
Active learning methods for neural networks are usually based on greedy criteria which ultimately give a single new design point for the evaluation.
Dropout-based Active Learning for Regression
Active learning is relevant and challenging for high-dimensional regression models when the annotation of the samples is expensive.
