Large language models (LLMs) have demonstrated the capacity to improve
summary quality by mirroring a human-like iterative process of critique and
refinement starting from the initial draft. Two strategies are designed to
perform this iterative process: Prompt Chaining and Stepwise Prompt. Prompt
chaining orchestrates the drafting, critiquing, and refining phases through a
series of three discrete prompts, while Stepwise prompt integrates these phases
within a single prompt. However, the relative effectiveness of the two methods
has not been extensively studied. This paper is dedicated to examining and
comparing these two methods in the context of text summarization to ascertain
which method stands out as the most effective. Experimental results show that
the prompt chaining method can produce a more favorable outcome. This might be
because stepwise prompt might produce a simulated refinement process according
to our various experiments. Since refinement is adaptable to diverse tasks, our
conclusions have the potential to be extrapolated to other applications,
thereby offering insights that may contribute to the broader development of
LLMs.

通过对文本摘要的上下文进行比较，研究和对比了两种迭代方法，即 Prompt Chaining 和 Stepwise Prompt，发现 Prompt Chaining 方法具有更好的效果，可能是因为它可以产生更多的优化过程。