  Large language models are powerful text processors and reasoners, but are
still subject to limitations including outdated knowledge and hallucinations,
which necessitates connecting them to the world. Retrieval-augmented large
language models have raised extensive attention for grounding model generation
on external knowledge. However, retrievers struggle to capture relevance,
especially for queries with complex information needs. Recent work has proposed
to improve relevance modeling by having large language models actively involved
in retrieval, i.e., to improve retrieval with generation. In this paper, we
show that strong performance can be achieved by a method we call Iter-RetGen,
which synergizes retrieval and generation in an iterative manner. A model
output shows what might be needed to finish a task, and thus provides an
informative context for retrieving more relevant knowledge which in turn helps
generate a better output in the next iteration. Compared with recent work which
interleaves retrieval with generation when producing an output, Iter-RetGen
processes all retrieved knowledge as a whole and largely preserves the
flexibility in generation without structural constraints. We evaluate
Iter-RetGen on multi-hop question answering, fact verification, and commonsense
reasoning, and show that it can flexibly leverage parametric knowledge and
non-parametric knowledge, and is superior to or competitive with
state-of-the-art retrieval-augmented baselines while causing fewer overheads of
retrieval and generation. We can further improve performance via
generation-augmented retrieval adaptation.
