Textual Out of Distribution Detection

Textual out-of-distribution (OOD) detection focuses on identifying text data that differs significantly from the data used to train a natural language processing (NLP) model. Current research emphasizes improving the robustness of OOD detection by exploring various methods for aggregating information from multiple layers of transformer-based models, rather than relying solely on the final layer's output. This involves developing novel techniques to learn more invariant and holistic sentence embeddings, often leveraging unsupervised learning approaches and addressing biases in existing representation learning frameworks. Effective OOD detection is crucial for ensuring the reliability and safety of deployed NLP systems, mitigating risks associated with unexpected or adversarial inputs.