Search Results for author:
Found 3 papers, 2 papers with code
Reconfidencing LLMs from the Grouping Loss Perspective
In this work, we construct a new evaluation dataset derived from a knowledge base to assess confidence scores given to answers of Mistral and LLaMA.
Beyond calibration: estimating the grouping loss of modern neural networks
Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities.
Benchmarking missing-values approaches for predictive models on health databases
Using gradient-boosted trees, we compare native support for missing values with simple and state-of-the-art imputation prior to learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
