Conversational Dense Retrieval

Conversational dense retrieval aims to improve information retrieval in multi-turn conversations by efficiently finding relevant information within a large corpus. Current research focuses on adapting large language models (LLMs) for robust query representation and leveraging techniques like contrastive learning and data augmentation (including synthetic data generation) to overcome the limitations of scarce training data. These advancements are significant because they promise more accurate and contextually aware search results, leading to improved user experience in conversational AI systems and potentially impacting various applications relying on natural language interaction.