  In long context scenarios, large language models (LLMs) face three main
challenges: higher computational/financial cost, longer latency, and inferior
performance. Some studies reveal that the performance of LLMs depends on both
the density and the position of the key information (question relevant) in the
input prompt. Inspired by these findings, we propose LongLLMLingua for prompt
compression towards improving LLMs' perception of the key information to
simultaneously address the three challenges. We conduct evaluation on a wide
range of long context scenarios including single-/multi-document QA, few-shot
learning, summarization, synthetic tasks, and code completion. The experimental
results show that LongLLMLingua compressed prompt can derive higher performance
with much less cost. The latency of the end-to-end system is also reduced. For
example, on NaturalQuestions benchmark, LongLLMLingua gains a performance boost
of up to 17.1% over the original prompt with ~4x fewer tokens as input to
GPT-3.5-Turbo. It can derive cost savings of \$28.5 and \$27.4 per 1,000
samples from the LongBench and ZeroScrolls benchmark, respectively.
Additionally, when compressing prompts of ~10k tokens at a compression rate of
2x-10x, LongLLMLingua can speed up the end-to-end latency by 1.4x-3.8x. Our
code is available at https://aka.ms/LLMLingua.
