This episode is focussing an academic paper by Microsoft introduces Generative Retrieval for Conversational Question Answering (GCoQA), a novel approach designed to enhance passage retrieval in conversational systems by addressing limitations found in traditional dual-encoder architectures. GCoQA utilizes an encoder–decoder framework to assign unique identifiers to passages and retrieves them by generating these identifiers token-by-token. The authors contend that this generative method overcomes the "embedding bottleneck" and facilitates more fine-grained, token-level interactions with the conversation context, which is crucial for handling ambiguous conversational queries. Experiments across three public datasets—OR-QuAC, QRECC, and TOPIOCQA—demonstrate that GCoQA achieves significant relative improvements in both passage and document retrieval accuracy, while also being notably more memory-efficient and faster than comparison methods. The paper concludes by discussing the method's practical implications, current limitations, and avenues for future research in generative retrieval.https://www.kopp-online-marketing.com/patents-papers/generative-retrieval-for-conversational-question-answering