Interactive Language

Interactive language processing (iNLP) focuses on creating language models that engage in dynamic, bidirectional conversations, moving beyond the limitations of traditional, turn-based systems. Current research emphasizes model architectures that enable real-time interaction, including full-duplex models capable of "listening while speaking" and active learning approaches that facilitate clarification through targeted questioning. This field aims to improve the accuracy, helpfulness, and user experience of language models by incorporating feedback and context-seeking behaviors, with implications for applications ranging from explainable AI to more natural human-computer interaction.