5 citations · 7 across the 3 of their papers we have counts for
4 papers
The Labeling Problem in Hallucination Detection Benchmarks: An Empirical Evaluation
Jorma Valjakka, Juhani Kivimäki, Juha Mylläri +1
In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked with open-domain question answ…
Performance Estimation in Binary Classification Using Calibrated Confidence
Juhani Kivimäki, Jakub Białek, Wojtek Kuberski +1
Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model's performance after deployment. Traditionally, perfor…
Confidence-based Estimators for Predictive Performance in Model Monitoring
Juhani Kivimäki, Jakub Białek, Jukka K. Nurminen +1
After a machine learning model has been deployed into production, its predictive performance needs to be monitored. Ideally, such monitoring can be carried out by comparing the mod…
Estimating Model Performance Under Covariate Shift Without Labels
Jakub Białek, Juhani Kivimäki, Wojtek Kuberski +1
After deployment, machine learning models often experience performance degradation due to shifts in data distribution. It is challenging to assess post-deployment performance accur…