  Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer
entity in a knowledge base which is several hops from the topic entity
mentioned in the question. Existing Retrieval-based approaches first generate
instructions from the question and then use them to guide the multi-hop
reasoning on the knowledge graph. As the instructions are fixed during the
whole reasoning procedure and the knowledge graph is not considered in
instruction generation, the model cannot revise its mistake once it predicts an
intermediate entity incorrectly. To handle this, we propose KBIGER(Knowledge
Base Iterative Instruction GEnerating and Reasoning), a novel and efficient
approach to generate the instructions dynamically with the help of reasoning
graph. Instead of generating all the instructions before reasoning, we take the
(k-1)-th reasoning graph into consideration to build the k-th instruction. In
this way, the model could check the prediction from the graph and generate new
instructions to revise the incorrect prediction of intermediate entities. We do
experiments on two multi-hop KBQA benchmarks and outperform the existing
approaches, becoming the new-state-of-the-art. Further experiments show our
method does detect the incorrect prediction of intermediate entities and has
the ability to revise such errors.
