# Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization

arXiv: https://arxiv.org/abs/2406.00507 · HTML: https://mlsanity.com/paper/2406.00507/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 1 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
- [Training Language Models with Language Feedback at Scale](https://mlsanity.com/paper/2303.16755/) (2023)
- [RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs](https://mlsanity.com/paper/2305.08844/) (2023)
- [Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization](https://mlsanity.com/paper/2311.09184/) (2023)
- [SummIt: Iterative Text Summarization via ChatGPT](https://mlsanity.com/paper/2305.14835/) (2023)

## Built on by (most cited first, 17 of 17)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT Applications](https://mlsanity.com/paper/2503.05346/) (2025)
- [Answer, Refuse, or Guess? Investigating Risk-Aware Decision Making in Language Models](https://mlsanity.com/paper/2503.01332/) (2025)
- [Reasoning or Not? A Comprehensive Evaluation of Reasoning LLMs for Dialogue Summarization](https://mlsanity.com/paper/2507.02145/) (2025)
- [ReFeed: Multi-dimensional Summarization Refinement with Reflective Reasoning on Feedback](https://mlsanity.com/paper/2503.21332/) (2025)
- [Exploring Iterative Controllable Summarization with Large Language Models](https://mlsanity.com/paper/2411.12460/) (2024)
- [Generating Query-Relevant Document Summaries via Reinforcement Learning](https://mlsanity.com/paper/2508.08404/) (2025)
- [TaleFrame: An Interactive Story Generation System with Fine-Grained Control and Large Language Models](https://mlsanity.com/paper/2512.02402/) (2025)
- [LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks](https://mlsanity.com/paper/2609.34187/) (2026)
- [Answering Questions in Stages: Prompt Chaining for Contract QA](https://mlsanity.com/paper/2410.12840/) (2024)
- [ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs](https://mlsanity.com/paper/2505.15410/) (2025)
- [Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers](https://mlsanity.com/paper/2510.10082/) (2025)
- [Scaling In, Not Up? Testing Thick Citation Context Analysis with GPT-5 and Fragile Prompts](https://mlsanity.com/paper/2602.22359/) (2026)
- [From Incomplete Architecture to Quantified Risk: Multimodal LLM-Driven Security Assessment for Cyber-Physical Systems](https://mlsanity.com/paper/2604.05674/) (2026)
- [Simulating Students or Sycophantic Problem Solving? On Misconception Faithfulness of LLM Simulators](https://mlsanity.com/paper/2605.12748/) (2026)
- [Imperfect Visual Verification for Code Edition : A Case Study on TikZ](https://mlsanity.com/paper/2606.15693/) (2026)
- [From Inline Notes to Collected Commentaries: Toward Context-Preserving Organization of Exegetical Knowledge in Classical Chinese Texts](https://mlsanity.com/paper/2607.29044/) (2026)

## Abstract (arXiv)

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.