  Retrieval-augmented generation models offer many benefits over standalone
language models: besides a textual answer to a given query they provide
provenance items retrieved from an updateable knowledge base. However, they are
also more complex systems and need to handle long inputs. In this work, we
introduce FiD-Light to strongly increase the efficiency of the state-of-the-art
retrieval-augmented FiD model, while maintaining the same level of
effectiveness. Our FiD-Light model constrains the information flow from the
encoder (which encodes passages separately) to the decoder (using concatenated
encoded representations). Furthermore, we adapt FiD-Light with re-ranking
capabilities through textual source pointers, to improve the top-ranked
provenance precision. Our experiments on a diverse set of seven knowledge
intensive tasks (KILT) show FiD-Light consistently improves the Pareto frontier
between query latency and effectiveness. FiD-Light with source pointing sets
substantial new state-of-the-art results on six KILT tasks for combined text
generation and provenance retrieval evaluation, while maintaining reasonable
efficiency.
