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Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model thatsets the state-of-the-art on many knowledge-intensive NLP tasks. However, thearchitecture used for FiD was chosen by making minimal modifications to astandard T5 model, which our analysis shows to be highly suboptimal for aretrieval-augmented model. In particular, FiD allocates the bulk of FLOPs tothe encoder, while the majority of inference time results from memory bandwidthconstraints in the decoder. We propose two simple changes to the FiDarchitecture to alleviate memory bandwidth constraints, and speed up inferenceby 7x. This allows us to use a much larger decoder at modest cost. We denoteFiD with the above modifications as FiDO, and show that it strongly improvesperformance over existing FiD models for a wide range of inference budgets. Forexample, FiDO-Large-XXL performs faster inference than FiD-Base and achievesbetter performance than FiD-Large.

FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference