  Retrieval augmented language models have recently become the standard for
knowledge intensive tasks. Rather than relying purely on latent semantics
within the parameters of large neural models, these methods enlist a
semi-parametric memory to encode an index of knowledge for the model to
retrieve over. Most prior work has employed text passages as the unit of
knowledge, which has high coverage at the cost of interpretability,
controllability, and efficiency. The opposite properties arise in other methods
which have instead relied on knowledge base (KB) facts. At the same time, more
recent work has demonstrated the effectiveness of storing and retrieving from
an index of Q-A pairs derived from text \citep{lewis2021paq}. This approach
yields a high coverage knowledge representation that maintains KB-like
properties due to its representations being more atomic units of information.
In this work we push this line of research further by proposing a
question-answer augmented encoder-decoder model and accompanying pretraining
strategy. This yields an end-to-end system that not only outperforms prior QA
retrieval methods on single-hop QA tasks but also enables compositional
reasoning, as demonstrated by strong performance on two multi-hop QA datasets.
Together, these methods improve the ability to interpret and control the model
while narrowing the performance gap with passage retrieval systems.
