4 papers
Online Safety Monitoring for LLMs
Mona Schirmer, Metod Jazbec, Alexander Timans +3
Despite alignment training, LLMs remain prone to generating unsafe outputs at deployment time. Monitoring outputs online and raising an alarm when safety can no longer be assumed i…
Aligning Recommendations with User Popularity Preferences
Mona Schirmer, Anton Thielmann, Pola Schwöbel +4
Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and…
Temporal Test-Time Adaptation with State-Space Models
Mona Schirmer, Dan Zhang, Eric Nalisnick
Distribution shifts between training and test data are inevitable over the lifecycle of a deployed model, leading to performance decay. Adapting a model on test samples can help mi…
Monitoring Risks in Test-Time Adaptation
Mona Schirmer, Metod Jazbec, Christian A. Naesseth +1
Encountering shifted data at test time is a ubiquitous challenge when deploying predictive models. Test-time adaptation (TTA) methods address this issue by continuously adapting a…