From RAG to RICHES - Retrieval Interlaced with Sequence Generation

Explore an innovative approach to integrating retrieval directly into sequence generation tasks in this 18-minute presentation by the Fellowship.ai team. Delve into the RICHES (Retrieval Interlaced with Sequence Generation) method, which simplifies conventional Retrieval-Augmented Generation (RAG) systems by merging retriever and generator functionalities. Learn how this unified approach eliminates the need for distinct components, adapts to new tasks through simple prompting, and operates with any instruction-tuned model without additional training. Discover the system's capabilities in supporting multi-hop retrievals, providing attributed evidence, and seamlessly planning retrieval steps within a single decoding pass of the Language Model. Gain insights into RICHES' robust performance across Open Domain Question Answering tasks, including attributed and multi-hop question answering. Access the related paper on Papers with Code and explore more tailored AI solutions for business challenges at Launchpad.ai.