  Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question
answering (QA) model that leverages passage retrieval with a pre-trained
transformer and pushed the state of the art on single-hop QA. However, the
complexity of multi-hop QA hinders the effectiveness of the generative QA
approach. In this work, we propose a simple generative approach (PathFid) that
extends the task beyond just answer generation by explicitly modeling the
reasoning process to resolve the answer for multi-hop questions. By linearizing
the hierarchical reasoning path of supporting passages, their key sentences,
and finally the factoid answer, we cast the problem as a single sequence
prediction task. To facilitate complex reasoning with multiple clues, we
further extend the unified flat representation of multiple input documents by
encoding cross-passage interactions. Our extensive experiments demonstrate that
PathFid leads to strong performance gains on two multi-hop QA datasets:
HotpotQA and IIRC. Besides the performance gains, PathFid is more
interpretable, which in turn yields answers that are more faithfully grounded
to the supporting passages and facts compared to the baseline Fid model.
