  Recent generative approaches for multi-hop question answering (QA) utilize
the fusion-in-decoder method~\cite{izacard-grave-2021-leveraging} to generate a
single sequence output which includes both a final answer and a reasoning path
taken to arrive at that answer, such as passage titles and key facts from those
passages. While such models can lead to better interpretability and high
quantitative scores, they often have difficulty accurately identifying the
passages corresponding to key entities in the context, resulting in incorrect
passage hops and a lack of faithfulness in the reasoning path. To address this,
we propose a single-sequence prediction method over a local reasoning graph
(\model)\footnote{Code/Models will be released at
\url{https://github.com/gowtham1997/SeqGraph}} that integrates a graph
structure connecting key entities in each context passage to relevant
subsequent passages for each question. We use a graph neural network to encode
this graph structure and fuse the resulting representations into the entity
representations of the model. Our experiments show significant improvements in
answer exact-match/F1 scores and faithfulness of grounding in the reasoning
path on the HotpotQA dataset and achieve state-of-the-art numbers on the
Musique dataset with only up to a 4\% increase in model parameters.
