Language Models as Reasoners for Out-of-Distribution Detection

Deep neural networks (DNNs) are prone to making wrong predictions with high confidence for data that does not stem from their training distribution. Consequentially, out-of-distribution (OOD) detection is important in safety-critical applications, as it identifies such inputs. Using prior knowledge about the training distribution through formal constraints has shown promise in enhancing OOD detection. However, developing and maintaining formal knowledge bases can be cumbersome. Large language models (LLMs) have recently excelled in various natural language processing tasks. In this study, we investigate the use of LLMs for OOD detection, where domain constraints are expressed in natural language. Our results indicate that LLMs can outperform random guessing by leveraging general world knowledge learned during training. Moreover, LLMs can par with methods based on formal constraints when supplemented with domain-specific constraints articulated in natural language.