Summary of Prompt-saw: Leveraging Relation-aware Graphs For Textual Prompt Compression, by Muhammad Asif Ali et al.
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
by Muhammad Asif Ali, Zhengping Li, Shu Yang, Keyuan Cheng, Yang Cao, Tianhao Huang, Guimin Hu, Weimin Lyu, Lijie Hu, Lu Yu, Di Wang
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This paper proposes PromptSAW, a novel approach to compress natural language prompts used in Large Language Models (LLMs) without sacrificing their utility. The authors observe that lengthy prompts come with significant costs, leading to substandard results. They build upon existing compression methods and develop a graph-based technique that extracts key information elements from the prompt’s textual information. PromptSAW achieves better readability and outperforms baseline models by up to 10.1% and 77.1% for task-agnostic and task-aware settings, respectively, while compressing the original text by 34.9% and 56.7%. This method can benefit various applications of LLMs in natural language processing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper finds a way to shorten long texts used with special computer models that understand human language. These computer models, called Large Language Models (LLMs), are very good at doing many things with text, but they get slower and less accurate when given very long instructions. The authors created a new method called PromptSAW that makes these long texts shorter while still making them useful for the computer model to understand. This new method works better than old ways of shortening text and can be used in many different applications where computers help with language tasks. |
Keywords
» Artificial intelligence » Natural language processing » Prompt
