Summary of Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization, by Shichao Sun et al.
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization
by Shichao Sun, Ruifeng Yuan, Ziqiang Cao, Wenjie Li, Pengfei Liu
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) have shown promise in improving summary quality by mimicking a human-like iterative process. Two strategies, Prompt Chaining and Stepwise Prompt, aim to replicate this process for text summarization. This paper investigates and compares these methods to determine which is more effective. Experimental results suggest that the prompt chaining method produces better outcomes, possibly due to its ability to simulate refinement processes. As refinement can be adapted to various tasks, our findings have implications for broader LLM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how two ways of using large language models (LLMs) can improve text summarization. The first way is called Prompt Chaining, and the second is Stepwise Prompt. Researchers tested these methods to see which one works best. They found that Prompt Chaining produces better summaries. This might be because it can simulate a process of refining ideas. Since this process can be applied to many different tasks, the results could help improve LLMs in general. |
Keywords
» Artificial intelligence » Prompt » Summarization
