WAISE

Our paper, Language Models as Reasoners for Out-of-Distribution Detection, was presented at the Workshop on AI Safety Engineering (WAISE) 2024 and received the best paper award by popular vote. It constitutes an extension of our idea of Out-of-Distribution Detection with Logical Reasoning, where we replaced the prolog-based reasoning component with an LLM. Abstract § Deep neural networks (DNNs) are prone to making wrong predictions with high confidence for data that does not stem from their …

My paper Towards Deep Anomaly Detection with Structured Knowledge Representations has been accepted on the Workshop on AI Safety Engineering at SafeComp. Abstract § Machine learning models tend to only make reliable predictions for inputs that are similar to the training data. Consequentially, anomaly detection, which can be used to detect unusual inputs, is critical for ensuring the safety of machine learning agents operating in open environments. In this work, we identify and discuss several …