Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
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| Format: | Preprint |
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2024
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| author | Ali, Muhammad Asif Li, Zhengping Yang, Shu Cheng, Keyuan Cao, Yang Huang, Tianhao Hu, Guimin Lyu, Weimin Hu, Lijie Yu, Lu Wang, Di |
| author_facet | Ali, Muhammad Asif Li, Zhengping Yang, Shu Cheng, Keyuan Cao, Yang Huang, Tianhao Hu, Guimin Lyu, Weimin Hu, Lijie Yu, Lu Wang, Di |
| contents | Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks. While prompting is a crucial tool for LLM inference, we observe that there is a significant cost associated with exceedingly lengthy prompts. Existing attempts to compress lengthy prompts lead to substandard results in terms of readability/interpretability of the compressed prompt, with a detrimental impact on prompt utility. To address this, we propose PromptSAW: Prompt compresSion via Relation AWare graphs, an effective strategy for prompt compression over task-agnostic and task-aware prompts. Prompt-SAW uses the prompt's textual information to build a graph and later extracts key information elements in the graph to come up with the compressed prompt. We also propose GSM8K-aug, i.e., an extended version of the existing GSM8K benchmark for task-agnostic prompts in order to provide a comprehensive evaluation platform. Experimental evaluation using benchmark datasets shows that prompts compressed by Prompt-SAW are not only better in terms of readability, but they also outperform the best-performing baseline models by up to 10.1 and 77.1, respectively, for task-agnostic and task-aware settings while compressing the original prompt text by 34.9 and 56.7. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00489 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression Ali, Muhammad Asif Li, Zhengping Yang, Shu Cheng, Keyuan Cao, Yang Huang, Tianhao Hu, Guimin Lyu, Weimin Hu, Lijie Yu, Lu Wang, Di Computation and Language Artificial Intelligence Machine Learning Large Language Models (LLMs) have shown exceptional abilities for multiple different natural language processing tasks. While prompting is a crucial tool for LLM inference, we observe that there is a significant cost associated with exceedingly lengthy prompts. Existing attempts to compress lengthy prompts lead to substandard results in terms of readability/interpretability of the compressed prompt, with a detrimental impact on prompt utility. To address this, we propose PromptSAW: Prompt compresSion via Relation AWare graphs, an effective strategy for prompt compression over task-agnostic and task-aware prompts. Prompt-SAW uses the prompt's textual information to build a graph and later extracts key information elements in the graph to come up with the compressed prompt. We also propose GSM8K-aug, i.e., an extended version of the existing GSM8K benchmark for task-agnostic prompts in order to provide a comprehensive evaluation platform. Experimental evaluation using benchmark datasets shows that prompts compressed by Prompt-SAW are not only better in terms of readability, but they also outperform the best-performing baseline models by up to 10.1 and 77.1, respectively, for task-agnostic and task-aware settings while compressing the original prompt text by 34.9 and 56.7. |
| title | Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2404.00489 |