Scope Detection

Scope detection, crucial for various AI applications like conversational agents and safety-critical systems, aims to identify inputs falling outside a model's intended operational domain. Current research focuses on improving the accuracy of out-of-scope detection, particularly for challenging cases like "hard-negative" examples that share features with in-scope data, employing techniques such as dual encoders, contrastive learning, and novel data augmentation strategies. These advancements are significant for enhancing the robustness and reliability of AI systems, leading to more effective and safer interactions between humans and machines.