  Recently, Chain-of-Thought (CoT) prompting has delivered success on complex
reasoning tasks, which aims at designing a simple prompt like ``Let's think
step by step'' or multiple in-context exemplars with well-designed rationales
to elicit Large Language Models (LLMs) to generate intermediate reasoning
steps. However, the generated rationales often come with mistakes, making
unfactual and unfaithful reasoning chains. To mitigate this brittleness, we
propose a novel Chain-of-Knowledge (CoK) prompting, where we aim at eliciting
LLMs to generate explicit pieces of knowledge evidence in the form of structure
triple. This is inspired by our human behaviors, i.e., we can draw a mind map
or knowledge map as the reasoning evidence in the brain before answering a
complex question. Benefiting from CoK, we additionally introduce a
F^2-Verification method to estimate the reliability of the reasoning chains in
terms of factuality and faithfulness. For the unreliable response, the wrong
evidence can be indicated to prompt the LLM to rethink. Extensive experiments
demonstrate that our method can further improve the performance of commonsense,
factual, symbolic, and arithmetic reasoning tasks.
