  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.
