PROMPT-SAW: Leveraging Relation-Aware Graphs for Textual Prompt
  Compression
Muhammad Asif Ali Zhengping Li Shu Yang Keyuan Cheng Yang Cao Tianhao Huang Lijie Hu Lu Yu Di Wang
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 sub-standard results in terms of readability and interpretability of the
compressed prompt, with a detrimental impact on prompt utility. To address
this, we propose PROMPT-SAW: 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,
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 14.3 and 13.7 respectively for task-aware and task-agnostic
settings while compressing the original prompt text by 33.0 and 56.7.