Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
Shichao Sun, Ruifeng Yuan, Ziqiang Cao, Wenjie Li, Pengfei Liu

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…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling · Biomedical Text Mining and Ontologies
