  Chain-of-Thought (CoT) prompting along with sub-question generation and
answering has enhanced multi-step reasoning capabilities of Large Language
Models (LLMs). However, prompting the LLMs to directly generate sub-questions
is suboptimal since they sometimes generate redundant or irrelevant questions.
To deal with them, we propose a GE-Reasoning method, which directs LLMs to
generate proper sub-questions and corresponding answers. Concretely, given an
input question, we first prompt the LLM to generate knowledge triplets, forming
a graph representation of the question. Unlike conventional knowledge triplets,
our approach allows variables as head or tail entities, effectively
representing a question as knowledge triplets. Second, for each triplet, the
LLM generates a corresponding sub-question and answer along with using
knowledge retrieval. If the prediction confidence exceeds a threshold, the
sub-question and prediction are incorporated into the prompt for subsequent
processing. This approach encourages that sub-questions are grounded in the
extracted knowledge triplets, reducing redundancy and irrelevance. Our
experiments demonstrate that our approach outperforms previous CoT prompting
methods and their variants on multi-hop question answering benchmark datasets.
