Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization

TL;DR

This paper compares prompt chaining and stepwise prompt strategies in LLM-based text summarization, finding prompt chaining generally yields better summaries due to more effective iterative refinement.

Contribution

It provides an empirical comparison of two iterative prompting strategies, highlighting the superior performance of prompt chaining in summarization tasks.

Findings

Prompt chaining produces higher quality summaries.

Stepwise prompt may simulate refinement but is less effective.

Results suggest prompt chaining's iterative process enhances output quality.

Abstract

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.