Results
The authors evaluated their technique on two benchmarks: the ProofWriter task (not shown) and EntailmentBankQA (shown). The technique increased accuracy substantially, especially on harder reasoning problems.

Implications
This paper illustrates a number of helpful lessons for improving the reliability of large language models:

Split complex tasks into smaller, more reliable subtasks
Generate your answer in a step-by-step fashion, evaluating it along the way
Generate many possible answers and use another model or function to pick the ones that look best
Reduce hallucination by constraining what the model can say (e.g., by using sentence labels instead of sentences)
Maximize performance of models by fine-tuning them on specialized tasks
To learn more, read the full paper.