What does the RAG Sequence model do in the context of generating a response?

The Retrieval-Augmented Generation (RAG) Sequence model operates by first using a retriever component to find a fixed set of relevant documents (top-K) from a knowledge base based on the entire input query. This set of documents is then passed, along with the original query, to a generator model (like BART or T5). The generator conditions its entire output sequence on this same set of documents, effectively treating them as a latent variable and marginalizing over them to produce a single, cohesive, and contextually-grounded response. This approach ensures the generated text is informed by the retrieved knowledge for its full duration.

A. The RAG-Sequence model retrieves a set of top-K documents, not a single one, to provide a richer and more robust context for the generator.
C. The retriever component processes the entire input query to capture the full user intent, not just an initial segment, ensuring the most relevant documents are found.
D. The standard RAG-Sequence model uses the original input query for retrieval. Query modification is a different, more advanced technique not inherent to the base RAG-Sequence architecture.

1. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems 33 (NeurIPS 2020). Section 2.1, "Models," describes the RAG-Sequence model: "The RAG-Sequence model approximates the marginal by treating the top-K documents as a latent variable, z, and summing over the sequence probabilities, conditioned on each document..." This confirms a set of documents is used together.

2. Oracle Cloud Infrastructure Documentation. (2024). Overview of Generative AI. Section: "Retrieval Augmented Generation (RAG)." The documentation explains the general RAG pattern used in OCI Generative AI, stating, "The RAG model takes the user prompt and retrieves relevant information from your enterprise data... The prompt and the retrieved text chunks are presented as context to the LLM..." This supports the concept of retrieving a set of information (chunks/documents) to generate a response.

3. Stanford University. (2023). CS224N: NLP with Deep Learning, Lecture 15: Open-domain Question Answering. Stanford Center for Professional Development. The lecture notes describe RAG as a system where "Given a question, we first use a retriever... to retrieve a few relevant text passages from a large corpus... Then, we use a reader... to read the retrieved passages and output an answer." This aligns with retrieving a set of documents ("a few relevant text passages") for a given query.