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Found 4 papers, 3 papers with code
Studying Large Language Model Behaviors Under Realistic Knowledge Conflicts
In cases where the models still fail to update their answers, we find a parametric bias: the incorrect parametric answer appearing in context makes the knowledge update likelier to fail.
Exploring Practitioner Perspectives On Training Data Attribution Explanations
Explainable AI (XAI) aims to provide insight into opaque model reasoning to humans and as such is an interdisciplinary field by nature.
Online Learning with Costly Features in Non-stationary Environments
Maximizing long-term rewards is the primary goal in sequential decision-making problems.
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems
Our proposed recommender system employs this policy to learn the users' item preferences online while minimizing runtime.
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